Automated logging of patching operations via mixed reality based labeling

ABSTRACT

A machine vision device is provided including a camera, a processor, and a device memory including computer program code stored thereon. The computer program code is configured, when executed by the processor, to receive an image, from the camera, including at least one readable digital label associated with communication equipment, determine if an anchor label is present in the image, receive equipment information based on the anchor label and generate a search matrix based on the equipment information and the anchor label The search matrix includes one or more search matrix locations of assets associated with the communication equipment.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of International Application No. PCT/US21/32845, filed on May 18, 2021, which claims the benefit of priority to U.S. Application No. 63/115,785, filed Nov. 19, 2020; U.S. Application No. 63/031,624, filed on May 29, 2020; U.S. Application No. 63/157,372, filed on Mar. 5, 2021; and U.S. Application No. 63/157,349, filed on Mar. 5, 2021, the content of which is relied upon and incorporated herein by reference in entirety.

TECHNICAL FIELD

This disclosure relates generally to optical connectivity, and more particularly to systems and methods for digitally tracking components in a network to enhance the physical layer knowledge of network components and their connectivity, and thereby enhance various installation, maintenance, upgrade, and/or de-commissioning operations associated with the network.

BACKGROUND

The large growth of the Internet has led businesses and other organizations to develop large scale data centers for organizing, processing, storing and/or disseminating large amounts of data. Data centers contain a wide range of information technology (IT) equipment including, for example, servers, networking switches, routers, storage systems, etc. Data centers further include a large amount of cabling and racks to organize and interconnect the IT equipment in the data center. Modern data centers may include multi-building campuses having, for example, one primary or main building and a number of auxiliary buildings in close proximity to the main building. IT equipment in the buildings on the campus is typically interconnected by one or more local fiber optic networks.

In order to organize the IT equipment in the data center, the buildings on the campus are typically divided into rooms or other identifiable physical spaces. Each room may then include multiple racks that are arranged in identifiable rows within the room. Each of the racks may then be further divided into housings/frames/shelves which hold the IT equipment. Each piece of IT equipment may include one or more ports for providing a data connection to another piece of IT equipment to form a network. For example, the racks may include a plurality of patch panels, each having a plurality of ports for making connections with other ports. Thus, the physical location of various ports in the data center may be designated by building, room, row, rack, housing/frame/shelf, panel, and finally port.

In an operational data center, connections are made between various ports of patch panels in order to connect different aspects of the data network. These connections may be made, for example, with a fiber optic or copper wire patch cord, which typically includes a length of fiber optic or copper wire cable terminated at both ends with a connector. The connectors of the patch cord are then inserted into certain ports in designated patch panels to achieve a desired connection between the IT equipment. Due to the very large number of connections and patch cords associated with a data center, the patch cords will generally be organized by routing the cords along the racks so as to extend generally vertically (e.g., upwardly) and along generally open raceways positioned above the racks. Such raceways provide a pathway for the patch cord to extend between two desired ports on different patch panels within a room or different rooms, for example.

During a greenfield installation in a data center, attempts are normally made to track the physical location of patch cords and create a log for tracking what ports the connectors of the patch cords are connected to. This information provides a description of the physical network structure referred to as the physical layer. The creation of a log with network components, their location, their connectivity in the data center, and their routing (if applicable), however, lacks standardization and is often done by various ad hoc approaches. As a result, in many cases, these logs are inaccessible (e.g., lost or misplaced), indecipherable (e.g., unreadable or uninterpretable), and/or incomplete (e.g., only a partial record or no record was ever made). For example, in one approach, the connectors of the patch cords may be labeled with an alphanumeric identifier, such as on a piece of tape wrapped around the cable adjacent its ends, or a barcode such as a one-dimensional, two-dimensional, or quick response (QR) code. Then, when a patch cord is inserted into a specific port in a patch panel in the data center, the connector identifier and a port identifier is manually logged into computer based spreadsheet, or paper-based records, such as a log book or 3-ring binder. This approach is sometimes referred to as “label and table”.

Such an approach, however, has many drawbacks. For starters, the manual entry of codes in a log is subject to human error. More particularly, the entries may have errors in the alphanumeric identifier and/or be difficult to decipher and interpret. Additionally, in some cases the tape or other ad hoc label may become separated from its patch cord or port, making a positive identification of a cable or port difficult, if not impossible. Furthermore, the original logs that record the physical location of the patch cords and data connections may over time become lost and/or unreliable. For example, in many cases the logs will not be updated during maintenance and/or upgrade operations. Thus, the logs may no longer provide an accurate and reliable indication of the physical location of network components and connections. Moreover, in many cases the installation of data centers is conducted on an accelerated schedule such that logs of the various network components and their physical connections in the data center architecture are simply not made at all. For one or more of these reasons, knowledge of the physical layer may be lacking.

The lack of an accurate physical layer log is problematic for several reasons. For example, various moves, additions, and changes in the data network, such as by maintenance, expansion, upgrade, or de-commissioning operations in the network, are a costly and time-consuming process. More particularly, data centers provide network services that impact a large number of customers. For instance, breakage or misconnection of patch cords in a data center may result in traffic interruption that affects a large number (e.g., millions) of end users. This in turn results in lost service, lost revenue, and frustrated and dissatisfied customers. Additionally, the traffic passing through a data center may include sensitive and high-value information that necessitates an extra layer of care to avoid errors, outages, or interruptions. Thus, a high level of care and competency is generally required when conducting changes in the physical layer of a data center.

The lack of available and accurate records of the physical layer complicates various network procedures, such as maintenance, upgrades, or de-commissioning operations. For example, considerable time and effort must be expended to effectively re-map the physical layer (e.g., the impacted patch cords, connectors, and ports) to ensure that changes in accordance with these procedures are done accurately and without loss of service to customers and end users. Accordingly, these processes are time and labor intensive, and generally increase the overall costs of the network change. In many cases, even if accurate records are available, network technicians responsible for maintenance or upgrade operations will avoid the risks associated relying on these records by installing new patch cords or other network components to achieve the maintenance or upgrade. This, however, is inefficient and also results in a generally costly and excessively congested process. Moreover, this further complicates the physical layer with additional network components and connections that are not generally recorded in the logs, in the event a log even exists. Accordingly, subsequent maintenance, upgrade, and de-commissioning operations may be even further complicated by inaccurate records of changes to the physical layer made during these operations.

The issues associated with a lack of trustworthy physical layer records occur not only with large data centers, but also occur in other areas of a distributed data network, such as a carrier network. For example, to meet modern demands for increased bandwidth and improved performance, carrier networks are increasingly providing optical fiber connectivity closer to end subscribers. These initiatives include fiber-to-the-node (FTTN), fiber-to-the-premises (FTTP), fiber-to-the-home (FTTH), fiber-to-the-antenna (FTTA), and the like (generally described as FTTx). In an FTTx network, for example, fiber optic cables are used to carry optical signals from a central location, such as a central office, to various distribution points and, in some cases, all the way to end subscribers. In many carrier networks, drop cables from subscriber premises are routed to a remote distribution point in the vicinity of the subscriber premises. The drop cables are then selectively connected to ports in the remote distribution point to achieve a connection with a desired service provider (e.g., phone, television, internet connectivity).

There are many different network architectures, and the various tasks required to distribute optical signals (e.g., splitting, splicing, routing, connecting subscribers) can occur at several locations. Regardless of whether a location is considered a local convergence point, network access point, demarcation point, or something else, network equipment is used to house components that carry out one or more of the tasks. The term “distribution point” will be used in this disclosure to generically refer to such equipment, which may include fiber distribution hubs (FDH), cabinets, closures, network interface devices, concentrators, multiplexers, distribution frames, local convergence cabinets, etc. Patch cords or other fiber optic cables may be used to make connections between various ports in a distribution point. Depending on the location where the distribution point is used in the carrier network, there may be hundreds of such connectors and ports in the distribution point.

As such, distribution points of a carrier network oftentimes suffer from the same drawbacks as connectors and ports in a data center, i.e., a log of the physical layer is often inaccessible, undecipherable, and/or incomplete. Accordingly, when a service technician accesses a distribution point to make a desired connection, in accordance with a work order, for example, there may be some uncertainty regarding the physical location of the distribution point associated with the work order, or whether the connectors and ports in the distribution point are being properly identified and connected. Of course, if the wrong connection is made, service to the end subscribers is interrupted or delayed and results in customer frustration and dissatisfaction. In many cases, the connections are being done by third-party contractors that are paid merely by the number of connections performed. Such an arrangement may promote errors, a lack of attention to good record keeping, and a lack transparency and accountability for when errors are made. Additionally, to correct the connection error, a service technician has to make a return trip to the distribution point to make the proper connection. Such return trips represent lost time and revenue and are generally costly to the service provider.

There have been some attempts to address cable management and connectively in various aspects of a data network through an automated approach. For example, radio frequency identification (RFID), near field communication (NFC) or other asset management tools have been used in some cases. These approaches, however, have generally failed to gain traction in the industry due to various reasons, including the incremental hardware costs associated with the tags and antennas required for these wireless technologies, the increased cost and complexity of the network infrastructure, the significant departure from current best practices and approaches, and a lack of robustness to the overall tracking system. Additionally, these approaches typically perform object recognition from more of a macro scale, and therefore may fail to differentiate between separate network components or parts when there are numerous tags/readers in very compact spaces, as is often the case in data center architectures and in distribution points of carrier networks, for example.

Based on the above, network service providers desire an improved approach for providing accurate information on the physical structure of data networks, whether that be within the context of a large data center, in a distribution point of a carrier network, or in a different aspect of the data network, where knowledge of the physical structure may prove beneficial. More particularly, there is a desire to have accessible and accurate information on any or all of the following: the identity, location, connectivity, and routing of various network components within the network.

SUMMARY

In an embodiment of the disclosure, a method of tracking network assets of a data network is provided. The method includes receiving an image including a first asset identifier associated with a first network asset, decoding the first asset identifier to produce a first identification code, and determining, based on the first identification code, a first network connection including the first network asset.

In an aspect of the disclosure, the first network connection is one of a virtual network connection defined in a database system or a physical network connection in the data network.

In another aspect of the disclosure, the image includes a second asset identifier associated with a second network asset, and the method further includes decoding the second asset identifier to produce a second identification code and identifying a second network asset based on the second identification code.

In another aspect of the disclosure, one of the first network asset and the second network asset is a port, and the other of the first network asset and the second network asset is a connector of a patch cord.

In another aspect of the disclosure, the first network connection includes the first network asset and the second network asset.

In another aspect of the disclosure, determining the first network connection based on the first identification code includes transmitting a query including the first identification code to the database system, and receiving a reply to the query from the database system including information defining the first network connection.

In another aspect of the disclosure, determining the first network connection based on the first identification code includes determining a first position of the first asset identifier in the image, determining a second position of the second asset identifier in the image, and determining, based on a relationship between the first position and the second position in the image, a presence of the first connection in the data network.

In another aspect of the disclosure, the first network connection includes the first network asset and the second network asset.

In another aspect of the disclosure, determining the first connection based on the first identification code further includes identifying a third network asset associated with the first identification code, the first network asset is a patch cord connected to the third network asset, and the first connection includes a connection between the second network asset and the third network asset through the first network asset.

In another aspect of the disclosure, the method further includes transmitting a query including the first identification code to the database system, receiving a reply to the query including data defining a second network connection, comparing the first network connection to the second network connection, and in response to the first network connection matching the second network connection, validating the first network connection in the data network.

In another aspect of the disclosure, the method further includes updating the database system to reflect the presence of the first network connection in the data network.

In another embodiment of the disclosure, another method of tracking network assets of the data network is provided. The method includes receiving an image including a first asset identifier having a first position in the image and a second asset identifier having a second position in the image. The method further includes decoding the first asset identifier to produce a first identification code, decoding the second asset identifier to produce a second identification code, and transmitting a query to a database system including the first identification code and the second identification code. In response to receiving a reply to the query including data defining a first network connection between a first network asset associated with the first identification code and a second network asset associated with the second identification code, the method adds a first virtual object that identifies the first network asset to a display, and adds a second virtual object that identifies the second network asset to the display.

In an aspect of the disclosure, the first virtual object and the second virtual object provide an indication that the first network asset is to be connected to the second network asset.

In another aspect of the disclosure, the first virtual object is a bounding box around a connector of a patch cord.

In another aspect of the disclosure, the method further includes adding one or more additional virtual objects to the display indicating a path of the patch cord.

In another embodiment, a method of tracking network assets of a data network includes receiving a first image including a first plurality of asset identifiers; receiving a second image including a second plurality of asset identifiers; decoding each of the first plurality of asset identifiers to produce a first plurality of identification codes; decoding each of the second plurality of asset identifiers to produce a second plurality of identification codes; identifying a first identification code associated with a first network asset that is included in both the first plurality of identification codes and the second plurality of identification codes; identifying a second identification code in the first plurality of identification codes associated with a second network asset connected to the first network asset; identifying a third identification code in the second plurality of identification codes associated with a third network asset connected to the first network asset; and determining, based on the connections between the first network asset and each of the second network asset and the third network asset, that the data network includes a first network connection between the second asset and the third asset.

In one embodiment, the first network asset may be a patch cable, the second network asset may be a first port, and the third network asset may be a second port. The first port may be located in a first rack, the second port may be located in a second rack, and the patch cord may run between the first rack and the second rack. The method may further include updating a database system to indicate the presence of the first connection in the data network.

In one embodiment, the method may further include updating the database system to indicate issuance of a work order to replace the first connection in the data network with a second connection in the data network connecting the second network asset to a fourth network asset. In this embodiment, the method may further include receiving a third image including a third plurality of asset identifiers; decoding each of the third plurality of asset identifiers to produce a third plurality of identification codes including the first identification code, the third identification code, and a fourth identification code associated with the fourth network asset; adding a first virtual object to a display that identifies the first network asset; adding a second virtual object to the display that identifies the fourth network asset; and adding a third virtual object to the display indicating the first network asset should be connected to the fourth network asset.

In yet another embodiment, a system for tracking a plurality of network assets of a data network is disclosed. The system includes one or more asset identifiers coupled to each of the plurality of network assets, a machine vision system configured to detect the one or more asset identifiers on the plurality of network assets, and a database system in communication with the machine vision system for storing and/or retrieving information on the plurality of network assets.

In one embodiment, at least one of the one or more asset identifiers may be view-point agnostic so that the machine vision system may identify the one or more asset identifiers from nearly any vantage point. In an exemplary embodiment, the one or more asset identifiers may include a machine-readable digital label so as to be readable by the machine vision system. For example, the asset identifiers may include colored bands, a barcode, textured surfaces, shapes, polarized-dependent elements, reflective, retroreflective and/or fluorescent elements. In an exemplary embodiment, the one or more asset identifiers may include a series of colored bands. The series of colored bands may include between 3 and 8 bands and between 3 and 6 colors, thus providing a wide range of coding diversity for coding a relatively large number of network assets. In one embodiment, the series of colored bands may include a directional key configured to indicate a read direction of the series of colored bands. Additionally, the series of colored bands may include a color key configured to provide color correction capabilities to the machine vision system.

In an exemplary embodiment, the machine vision system may include one or more cameras. The machine vision system and its cameras may be a fixed part of the data network, such as being attached to certain fixed hardware (e.g., racks, cabinets, etc.) associated with the data network. Alternatively, the machine vision system may form part of a robotic system associated with the data network. In yet a further embodiment, the machine vision system may be configured as a portable device adapted to be carried by a human user. In this embodiment, the machine vision system may include a display for use by the human user. For example, the machine vision system may include a handset or a headset device configured to be carried by the human user. In specific instances, the handset device may include a mobile phone or a tablet, and the headset device includes glasses, goggles or a helmet. These devices may include augmented reality features.

The database system is configured to store information or retrieve information on the plurality of network assets in the data network. This will allow a technician to then access information about the data network during, for example, a maintenance or upgrade operation. In an exemplary embodiment, the database system may be configured in the cloud, and thus readily accessible by machine vision systems at a wide range of work sites. In one embodiment, the system may form part of an asset tracking system, an inventory management system, or a network management system.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are included to provide a further understanding and are incorporated in and constitute a part of this specification. The drawings illustrate one or more embodiment(s), and together with the description serve to explain principles and operation of the various embodiments. Features and attributes associated with any of the embodiments shown or described may be applied to other embodiments shown, described, or appreciated based on this disclosure.

FIG. 1 is a schematic illustration of a data center campus in which embodiments of the disclosure may be used;

FIG. 2 is a schematic illustration of a room in the buildings of the data center campus including IT equipment;

FIG. 3 is a schematic illustration of a carrier network in which embodiments of the disclosure may be used;

FIG. 4 is a schematic illustration of a distribution point of the carrier network illustrated in FIG. 3 ;

FIG. 5 is a schematic illustration of a system in accordance with an embodiment of the disclosure for tracking network assets in a data network;

FIG. 6A is an illustration of a network asset in the form of a patch cord in accordance with an embodiment of the disclosure;

FIGS. 6B-6F is an illustration of network assets in a data network including various label carriers in accordance with an example embodiment;

FIGS. 7A and 7B are schematic illustrations of network assets in the form of a port on a patch panel in accordance with an embodiment of the disclosure;

FIG. 8 is a schematic illustration of a machine vision system in accordance with an embodiment of the disclosure;

FIG. 9 is a schematic illustration of a computing device in accordance with an embodiment of the disclosure;

FIG. 10 is a schematic illustration of a portion of a data network including a plurality of racks in accordance with an embodiment of the disclosure;

FIG. 11A is a view of a user using a machine vision system to capture an image of a network asset in accordance with an embodiment of the disclosure;

FIG. 11B is a view of the captured image of FIG. 11A in accordance with an example embodiment;

FIG. 11C is a view of the captured image including a search matrix in accordance with an example embodiment;

FIG. 12 is a schematic view of a flowchart depicting a process associated with carrying out a work order in accordance with an embodiment of the disclosure;

FIG. 13 is a schematic view of a flowchart depicting a process for decoding asset identifiers in accordance with an embodiment of the disclosure;

FIG. 14 is a schematic view of an augmented reality display in accordance with an embodiment of the disclosure;

FIG. 15 is a schematic view of an augmented reality display in accordance with another embodiment of the disclosure;

FIG. 16 is a schematic view of a flowchart depicting a process of verifying network assets using an anchor label and search matrix. in accordance with another example embodiment of the disclosure;

FIG. 17 illustrates an image captured by a machine vision system during verification of a network assets in accordance with an embodiment of the disclosure;

FIG. 18 illustrates a equipment chassis including a plurality of independently translatable trays and modules in accordance with an embodiment of this disclosure;

FIGS. 19 and 20 illustrate a flow chart of a method for guided installation of a network asset using mixed reality in accordance with an embodiment of this disclosure;

FIGS. 21-23 illustrate views of a guided installation of a connector into to a port associated with the equipment chassis of FIG. 18 in accordance with an embodiment of this disclosure;

FIG. 24 illustrates a module including a plurality of very small form factor connectors having asset identifiers in accordance with an embodiment of this disclosure;

FIGS. 25A-25C illustrate examples of network connectivity architectures in accordance with an embodiment of this disclosure;

FIGS. 26A-26D illustrate example network connection densities in accordance with an embodiment of this disclosure;

FIG. 27 illustrates an flow chart of a method of providing connectivity tracing in a network in accordance with an embodiment of this disclosure;

FIG. 28 illustrates a cable assembly in accordance with an example embodiment of this disclosure; and

FIG. 29 illustrates a shuffle matrix in accordance with an example embodiment of this disclosure.

DETAILED DESCRIPTION

As described in the background, manual tracking of network assets of a data network may be difficult, time intensive, and prone to introduction of error. In some network environments, such as in active portions of a communications network, network assets may be identified by mapping Media Access Control (MAC) addresses of the active network assets. Using the network map, digital renderings may be made of connections within the network. However, passive network assets, such as fiber optic patch panels, patch cables, connectors, ports, and the like do not have a MAC or a method of actively mapping these assets. Below a system and method for automatically logging, associating, and tracking of passive network assets is provided.

As illustrated in FIG. 1 and in one application of the present disclosure, a modern-day data center 10 may include a collection of buildings (referred to as a data center campus) having, for example, a main building 12 and one or more auxiliary buildings 14 in close proximity to the main building 12. While three auxiliary buildings are shown, there may be more or less depending on the size of the campus. The data center 10 provides for a local network 16 that interconnects the auxiliary buildings 14 with the main building 12. The local network 16 allows IT equipment 18 in the main building 12 to communicate with various IT equipment (not shown) in the auxiliary buildings 14. In the exemplary embodiment shown, the local network 16 includes trunk cables 20 extending between the main building 12 and each of the auxiliary buildings 14. Trunk cables 20 generally include a high fiber-count arrangement of optical fibers for passing data and other information through the local network 16. Within the main building 12, a plurality of indoor fiber optic cables 22 (“indoor cables 22”) are routed between the IT equipment 18 and one or more distribution cabinets 24. The indoor cables 22 may also generally include a high fiber-count arrangement of optical fibers for passing data and other information through the local network 16. Although only the interior of the main building 12 is schematically shown in FIG. 1 , each of the auxiliary buildings 14 may house similar IT equipment 18 for similar purposes.

As discussed above and illustrated in FIG. 2 , in a typical data center configuration, the IT equipment 18 in the buildings 12, 14 is arranged in rooms 26 within the buildings 12, 14. In a conventional arrangement, each of the rooms 26 further includes a plurality of racks 28 arranged in rows 30 within the room 26. Each of the racks 28 may include a plurality of housings/frames/shelves 32 for holding the IT equipment 18 and providing an interface for making data connections with the IT equipment 18. By way of example, the housings 32 of the racks 28 may include one or more patch panels 34 or other network interfaces having one or more ports 36 for making a data connection. As demonstrated in this figure, each of the rooms 26 of the data center 10 typically includes a large number of ports 36 for making data connections between the different IT equipment 18 in the data center 10. To facilitate data connections between ports 36 in the data center 10, e.g., typically within the same room 26 or between ports 36 in adjacent rooms 26, relatively short connectorized cables (e.g., fiber optic or copper wire cables), referred to herein as patch cords 38, may be provided. Accordingly, each of the rooms 26 in the data center 10 typically includes a large number of patch cords 38 for achieving the data connections between selected ports 36 on the patch panels 34. The patch cords 38 are generally organized into groups and extend along raceways 40 positioned above the racks 28. As discussed above, due to the large number of ports 36 and patch cords 38 in the rooms 26 of the data center 10, an improved manner of tracking network components in the physical layer, including identifying the network components, knowing their location within the data center 10, knowing their connectivity to other network components, and knowing their routing pathway is desirable.

In FIG. 3 and in another application of the present disclosure, a schematic diagram of an exemplary FTTx carrier network 42 distributes optical signals generated at a switching point 44 (e.g., a central office of a network provider) to subscriber premises 46. Optical line terminals (OLTs; not shown) at the switching point 44 convert electrical signals to optical signals. Fiber optic feeder cables 48 then carry the optical signals to various local convergence points 50, which act as locations for splicing and making cross-connections and interconnections. The local convergence points 50 often include splitters to enable any given optical fiber in the fiber optic feeder cable 48 to serve multiple subscriber premises 46. As a result, the optical signals are “branched out” from the optical fibers of the fiber optic feeder cables 48 to optical fibers of distribution cables 52 that exit the local convergence points 50.

At remote distribution points closer to the subscriber premises 46, some or all of the optical fibers in the distribution cables 52 may be accessed to connect to one or more subscriber premises 46. Drop cables 54 extend from the distribution points to the subscriber premises 46, which may be single-dwelling units (SDU), multi-dwelling units (MDU), businesses, and/or other facilities or buildings. A conversion of optical signals back to electrical signals may occur at the remote distribution points or at the subscriber premises 46.

FIG. 4 illustrates one example of a distribution point 56 to be placed at one of the local convergence points 50 in FIG. 3 . The distribution point 56 may be in the form of a cabinet or enclosure that includes a frame 58 installed in a housing (not shown). The frame 58 supports various components for accomplishing the network tasks associated with the local convergence point 50. For example, a row of fiber optic ports 60 supported by the frame 58 defines a feeder field 62 to receive connections associated with one of the feeder cables 48 of the network 42. Optical fibers (not shown) from the feeder cable 48 may be terminated with fiber optic connectors (directly or by splicing to pigtails) that are plugged into the fiber optic ports 60 on the back side of the frame 58.

Below the feeder field 62, the frame 58 defines a region 64 for receiving and supporting splitter modules 66 (e.g., in slots provided in the frame 58). The splitter modules 66 may each include an input cable 68 and a plurality of output cables 70. The splitter modules 66 each include an optical splitter (not shown) so that a multiplexed signal carried by the input cable 68 can be separated into demultiplexed signals carried by the output fibers of the output cables 70. The multiplexed signal typically comes from the feeder cable 48. To this end, the input cable 68 of the splitter modules 66 may be terminated with a fiber optic connector and plugged into the front side of the fiber optic ports 60 in the feeder field 62, thereby establishing data connections with the feeder cable 48.

The output cables 70 that are “live” (i.e., used in the network to carry signals to and from subscribers) are plugged into the front side of fiber optic ports 60 in a distribution field 72. There are typically several or many rows of ports 60 defining the distribution field 72. These ports 60 are used to establish data connections with optical fibers of one or more distribution cables 52 or drop cables 54 that exit the distribution point 56 and carry signals further into the carrier network 42 so that ultimately the signals can reach the subscriber premises 46. The output cables 70 that are not used for live network traffic (e.g., “dark fiber”), and instead are reserved for future subscribers, may be routed to a storage location 74 for use at a later date. Similar to the above, due to the large number of ports 60 and cables 68, 70 in the distribution points 56 of the FTTx carrier network 42, an improved manner of tracking the network components (ports 60 and cables 68, 70) in the physical layer, including identifying the network components, knowing their location within the carrier network 42, knowing their connectivity to other network components, and knowing their routing pathway is desirable.

While the particular network environment for aspects of the present disclosure discussed below includes a data center 10 and carrier network 42, aspects of the present disclosure are not limited to these environments. Most any aspect of a data network where traceability of network components proves advantageous may provide the suitable environment for implementation of aspects of the present disclosure. For example, aspects of the present disclosure may be beneficial in passive optical networks (PON), access networks, optical line terminals (OLT) or optical network units (ONU) in an access or PON network, multiplexers, switches, routers, or other suitable aspect of the data network. Accordingly, aspects of the present disclosure should not be limited to data center or carrier network applications as described herein.

In accordance with an embodiment of the disclosure and as illustrated in FIG. 5 , a system 80 that facilitates the digital tracking of network components (referred to generally herein as “network assets” 82) in a data network is illustrated. System 80 may operate as a stand-alone digital tracking system, or may be integrated with another system, such as an asset management system, network management system, an inventory management system, or any other suitable system where information relating to system assets is relevant. The term tracking is meant to encompass information including but not limited to asset identification, asset location, and asset connectivity (i.e., taken collectively, information pertaining to the physical layer of the data network). The system 80 includes one or more asset identifiers 84 on or otherwise associated with the network assets 82, a machine vision system 86 for ascertaining information about the network assets 82 from the asset identifiers 84, and a database system 88 for storing and selectively accessing information about the physical layer of the data network. Machine vision refers to guidance using hardware and software to receive and process images. As used herein, a network asset 82 includes any component which may be used to implement the physical layer of a network. Thus, network assets 82 encompass a wide range of components or portions of components used in the data network. By way of example and without limitation, in one embodiment the network asset 82 may include a patch panel 34, ports 36 of a patch panel 34, patch cords 38, and/or connectors of patch cords 38. In an alternative embodiment, the network asset 82 may include input cables 68, output cables 70, connectors associated with cables 68, 70, ports 60 in the feeder field 62, and/or ports 60 in a distribution field. These are merely exemplary and other components of the data network may serve as network assets 82. For example, a port or a pigtail in a PON network, a port on a wavelength division multiplexing (WDM) multiplexer, an element of a converged access network, an OLT or ONU port in an access/PON network, a power port, or an ethernet port on a switch or router may also serve as a network asset in accordance with aspects of the disclosure. Thus, a wide range of network components or portions thereof may serve as a network asset 82 in the context of the present disclosure and therefore should not be limited to any particular one or group of components or elements. Other example may include, without limitation a point to point access network, a splitter based network, and a WDM based network, and a WDM Radio Access Network (RAN) connecting 4G and 5G antennas.

In accordance with an aspect of the present disclosure, each network asset 82 may include one or more asset identifiers 84 associated with the network asset 82 that enable tracking of the network asset 82 in the data network. In embodiments of the disclosure, an asset identifier 84 may include a machine-readable digital label 90, such as barcodes color codes QR codes, or other suitable readable labels, configured to be “read” by the machine vision system 86 of the system 80. Because the machine vision system 86 is configured to read asset identifiers 84 in its field of vision (possibly one, a few, or many asset identifiers 84), it may be advantageous for the asset identifiers 84 to be decodable from a wide range of angles and in a wide range of lighting conditions.

Thus, in an aspect of the disclosure, the asset identifiers 84 are configured to be view-point agnostic or omni-directional, and thus be visible to and decodable by the machine vision system 86 from nearly any angle. This feature is a departure from many labels (e.g., such as barcodes) used in current automated systems, which often require a flat presentation of the label and a clear field of vision by an adjacent reader. In an exemplary embodiment, a view-point agnostic identifier may be achieved, for example, by wrapping the machine-readable digital label 90 completely around (e.g., a substantially full 360°) at least a portion of the network asset 82. By way of example, the machine-readable digital label 90 may be wrapped about a portion of the network asset 82 in a substantially cylindrical or conical configuration. As can be appreciated, these geometric configurations allow the machine-readable digital label 90 to be decoded when viewed by the machine vision system 86 from large range of angles or many vantage points.

By way of example, FIG. 6A illustrates a network asset 82 in the form of a patch cord 38 which includes at least one, and preferably a plurality of machine-readable digital labels 90 attached thereto that are view-point agnostic. As illustrated in this figure, the patch cord 38 includes an intermediate length of cable 92 having a first end 94 terminated by a connector 96 and a second end 98 terminated by another connector 100. Such patch cords 38 and their construction are generally well known in the art and a further description of the conventional features of the patch cord 38 and its construction will not be discussed here. In a similar manner, a wide range of connectors 96, 100 and their construction are generally known in the art and a further description of those connectors will not be discussed here.

In an exemplary embodiment, each connector 96, 100 of the patch cord 38 may include a machine-readable digital label 90. The label 90 may be located on most any aspect of the connectors 96, 100 so long as they are viewable from substantially any angle relative to the machine vision system 86. In one embodiment, for example, the machine-readable digital labels 90 may be disposed about the strain-relief features of the connectors 96, 100. In an alternative embodiment, the machine-readable digital labels 90 may be disposed on the outer sheath of the intermediate cable 92 adjacent the connectors 96, 100. In addition, the patch cord 38 may include one or more machine-readable digital labels 90 disposed along the length of the intermediate cable 92. By way of example, a machine-readable digital label 90 may be disposed about the outer sheath of the intermediate cable 92 at select positions along the length of the intermediate cable 92. Alternatively, a machine-readable digital label 90 may be disposed about the outer sheath of the cable 92 at a regular interval along the cable 92 (e.g., every meter). This allows the connectors 96, 100 of the patch cord 38 (end points of the patch cord 38), and possibly points intermediate the connectors 96, 100 of the patch cord 38 to be recognized by the machine vision system 86. This, in turn, provides end point traceability and pathway traceability of the patch cord 38. In one embodiment, all of the machine-readable digital labels 90 associated with a particular network asset 82 (e.g., patch cords 38) may be the same type. In an alternative embodiment, however, the labels 90 associated with the same network asset 82 may be different types.

The machine-readable digital label 90 may take several forms. By way of example, the machine-readable digital label 90 may include a series of colored bands or colored patterns, a barcode, textured surfaces, shapes, or colors not visible to humans (e.g., ultra-violet light in the 400-450 nanometer (nm) wavelength range or infrared light in the 650-900 nm wavelength range). Furthermore, the machine-readable digital label 90 may include fluorescent and/or polarization-dependent coding elements. Polarization-dependent elements reflect incident light with a change in its polarization such as increasing the degree of polarization, converting linearly polarized light to elliptical or circular light. Fluorescent-dependent coding elements refers to materials in which the light emitted is generated from an external light source at a different frequency or wavelength. Aspects of the disclosure are not limited to these exemplary configurations as other characteristics may be used to provide differentiating features that used alone or in combination may be used as a unique identifier.

As illustrated in FIG. 6A, in one embodiment, the machine-readable digital label 90 may include a series of colored bands 102 adjacent each other. The coding diversity of the machine-readable digital label 90 is provided by the number of bands 102 and the number of colors used in the bands 102. For example, in one embodiment the machine-readable digital label 90 may include between 3 and 8 bands 102 with between 3 and 6 colors. The number of unique identification codes, or “identifiers” is generally given by M^(N), where M is the number of colors and N is the number of bands 102 in the machine-readable digital label 90. Thus, for a machine-readable digital label 90 using 8 bands and 6 colors, there are a total of 1,679,616 unique identifiers. In other applications, such a large number of identifiers may not be necessary and a machine-readable digital label 90 having 6 bands with 3 colors (total of 729 unique identifiers) may be more appropriate. It should be recognized that other numbers of bands and colors may be used depending on the particular application and the number of bands and colors may be selected to provide the desired coding diversity required in a particular application.

In one embodiment in accordance with the disclosure, the machine-readable digital label 90 may be configured to provide a read direction for the label. For example, in the case of the machine-readable digital label 90 being a series of colored bands 102 on a patch cord 38, the label 90 may be read from a starting point closest to the connectors 96, 100 or furthest from the connectors 96, 100. The read direction may affect decoding of the label 90 due to possible symmetries in the color sequence of the machine-readable digital label 90. To maximize the possible number of unique identifiers and increase identification code diversity, a read direction of the machine-readable digital label 90 may be established. In one embodiment, a read direction of the label 90 may be established by using one of the bands 102 in the sequence of bands 102 as a directional key. For example, either the first or last band 102 (i.e., a predetermined location) in the sequence of bands 102 may have a dedicated color or feature distinguishable by the machine vision system 86 that indicates the read direction (e.g., starting/stopping point) of the machine-readable digital label 90. Those of ordinary skill in the art may well recognize other features of the machine-readable digital label 90 that may operate as a directional key for the label 90.

In addition to enabling a greater number of unique identifiers, increasing identification code diversity may also allow for increased minimum Hamming distances between the unique identifiers used in a particular network. For example, the unique identifiers used within a particular network may be selected from the set of all possible unique identifiers so that the minimum Hamming distance between the unique identifiers is greater than one. This increased minimum Hamming distance may enable the machine vision system 86 to detect and/or correct errors made decoding asset identifiers 84. This ability to detect and/or correct decoding errors may in turn enable the machine vision system 86 to correctly decode asset identifiers 84 which are partially obstructed, thereby improving system reliability and performance.

In another embodiment in accordance with the disclosure, the machine-readable digital label 90 may be configured to provide color correction capabilities. This may be important because images of the asset identifier 84 may occur in a wide range of ambient lighting, thereby affecting the image of the asset identifier 84. For example, in the case of the machine-readable digital label 90 being a series of colored bands 102 on a patch cord 38, the variation of the ambient lighting, shading and possibly other effects in an image captured by the machine vision system 86 may cause the colors of the bands 102 to appear differently. To address this variability, at least one of the bands 102 may be used to provide color calibration.

For example, a band 102 in a predetermined location in the machine-readable digital label 90 may be a known, dedicated color, such as a neutral white or grayscale color, and thus be used as a color key. The deviations in the color key between the color of the key in the captured image and the key's known color characteristics provides a color correction factor that may be applied to the remaining bands 102 in the machine-readable digital label 90. This will allow captured color images having variable and uncontrolled ambient lighting or other effects to be corrected to provide a more accurate color sequencing of the machine-readable digital label 90. In one embodiment, the directional key and the color key may be provided by different bands 102 of the machine-readable digital label 90. In an alternative embodiment, however, the directional key and the color key may be provided by the same band 102 of the machine-readable digital label 90.

In one embodiment, the asset identifier 84 may be incorporated into the network asset 82 as part of the manufacturing of the asset 82. By way of example, colored inks and printing techniques or colored injection molded plastics may be used to form the colored bands 102 of the machine-readable digital label 90 during manufacture of the network asset 82 (e.g., patch cord 38). Alternatively, the asset identifier 84 may be added to the network asset 82 as a separate process and after manufacture of the asset 82. For example, colored films or tapes may be adhesively or thermally bonded to the network asset 82 after manufacturing and prior to or during installation of the network asset 82. Asset identifiers 84, such as machine-readable digital labels 90, may be added to existing network assets 82, such as in a retrofit or brownfield application. Those of ordinary skill in the art may recognize other processes for incorporating the asset identifier 84 with the network asset 82 that are within the scope of the present disclosure.

FIGS. 7A and 7B illustrate a network asset 82 in the form of a port 36 in a patch panel 34 and includes at least one machine-readable digital label 104 associated with the port 36. For example, each port 36 in the patch panel 34 may be associated with one machine-readable digital label 104. The machine-readable digital labels 104 may be positioned adjacent their respective ports 36, such as immediately above or beside their respective ports 36 (including around their respective ports 36). In one embodiment illustrated in FIG. 7A, the machine-readable digital labels 104 may not be configured as view-point agnostic. Instead, the machine-readable digital labels 104 may be positioned generally flat along the patch panel 34. For example, the machine-readable digital labels 104 may be provided by stickers, placards, etc. that are adhered or otherwise attached to the patch panel 34 adjacent their corresponding ports 36. In one embodiment, for example, the flat machine-readable digital label 104 may include a series of colored stripes 106 similar to the colored bands 102 discussed above. In an alternative embodiment illustrated in FIG. 7B, however, the machine-readable digital labels 104 may be configured as view-point agnostic similar to the machine-readable digital label 90 associated with the patch cord 38. By way of example, a cylindrical or conical-shaped post 108 may be positioned adjacent each of the ports 36 of the patch panel 34. Similar that that described above, each of the posts 108 may include a plurality of colored bands 102 extending around the post 108 and may be decodable by the machine vision system 86 from most any vantage point.

Turning to FIG. 6B-6D, in some example embodiments, for example datacenters with high connection density, it may be advantageous to dispose the readable digital labels in a plane offset from the front of the patch panel 34. The offset may enable fiber routing behind the readable digital label 90, thereby limiting or preventing interference with reading of the digital readable labels 90 by poor fiber routing or grooming. The example depicted in FIG. 6B, a vertical label mount 91 extends from the boot of the connector 96. The label mount 91 may extend substantially perpendicular to the direction of insertion and removal of the connector 96. The label mount 91 may be integral to the boot of the connector 96 or may be clipped on to the boot of the connector 98. FIG. 6C depicts an example connector 96 including a longitudinal extension 101. In some example embodiments, the longitudinal extension 101 may be a push/pull actuator for installation and removal of the connector 96. The readable digital label 90 may be disposed on the end of the longitudinal extension 101. In the example depicted in FIG. 6D, the connector may include a label carrier 103. The label carrier 103 may be integral to the connector 96, such as injection molded with, or may be clipped to the housing of the connector 96. The label carrier 103 may extend longitudinally from a top of a connector 96. The label carrier 103 may include a label face 105, configured to display an attached readable digital label 90. The label face 105 may extend perpendicularly toward the connector side of the label carrier 103 at an offset distance from the rear of the connector 96. The offset distance may be sufficient to enable routing of optical fibers between the connectors 96 and the label face 105, such that the label face 105 is not obstructed by an optical fiber during reading, as described below.

Referring to FIGS. 6E and 6F, datacenters that include communication with modules, trays, cassettes, or the like, may enable horizontal reading of digital labels. For example, the digitals labels may be readable when the tray module, cassette of the like is extended from an operating to a service position. FIG. 6E depicts an example connector 96 including a digital readable label disposed on the connector boot or extension of the connector housing. FIG. 6F depicts a connector 96 including a label carrier 107 similar to the label carrier 103 depicted in FIG. 6D. However, the label face 105 has be modified to include a label cube 109. The label cube may include label faces on the back, top, bottom first side, and or the second side. The label cube may enable reading of the digital readable label from multiple angles.

FIG. 8 depicts a machine vision system 86 in accordance with an aspect of the disclosure and generally includes a computer 110 operably coupled to a display 112, a communication module 114, and a camera 116 having a field of view 118 for viewing one or more network assets 82, and more particularly, the asset identifier 84 thereof, associated with a data network or a portion thereof. The field of view 118 of camera 116 may be similar to that of the user so that the machine vision system 86 receives images corresponding to what the user is viewing. The machine vision system 86 may also include or receive images from additional cameras (not shown) in order to identify network assets 82 that are not within the field of view 118 of camera 116. The camera 116 may be a device that receives images or information from its surrounding environment (e.g., visual images, IR sensor, photosensor, or the like).

Using machine vision to identify network assets 82 provides several advantages over conventional methods of managing the physical layer. The ubiquity of smart phones and the emergence of headsets, wearable and smart glasses that include a high-quality camera and Internet communication capabilities may allow a technician to use the machine vision system by merely downloading a machine vision application into their smart phone. This feature avoids the need to provide specialized equipment to technicians in the field. The lack of specialized equipment could enable a technician, for example, to download the application to their smart phone on a moment's notice (e.g., while talking to a dispatcher at a network operations center), take a picture of the network asset at issue, and resolve a network connection problem without leaving the worksite. As such, aspects and methods in accordance with the disclosure are primarily (other than the asset identifier 84) software based. Accordingly, the machine vision system 86 may thereby reduce both costs and the amount of time required to resolve physical layer network issues as compared to known systems.

In one embodiment, the machine vision system 86 may be a fixed part of the data network hardware, such as being mounted to racks 28 within the rooms 26 of a data center 10 or being mounted to various distribution points 56 of a carrier network 42. In an alternative embodiment, however, the machine vision system 86 may be configured as a portable device, e.g. a machine vision device, that is, for example, carried or worn by a service technician or other user. In one embodiment, the portable device may include various types of hand units or head units. By way of example, this may include a portable smartphone or tablet computer running a machine vision application, or glasses, goggles, helmets or other headsets running a machine vision application. In yet another embodiment, the machine vision system 86 may be integrated into another device, such as a robot or other automated aspect of an automated management system, for monitoring or changing the physical layer of the data network.

The display 112 may include a screen on the user device, a head-mounted display, an optical head-mounted display, or any other device suitable for displaying visual information to a user. In embodiments where the machine vision system 86 is integrated into a robot, the display 112 may be omitted. The machine vision system 86 may display video in real time on the display 112 showing the network assets 82 within the field of view 118. In a further aspect of the disclosure, and as will be discussed in more detail below, the machine vision system 86 may enhance the video with computer-generated graphics that provide information to the user, such as indicators which identify network assets 82 and their connectivity, e.g., what ports 36 the connectors 96, 100 of a patch cord 38 are to be connected to and instructing or otherwise guiding the user to make those connections.

The communication module 114 may be configured to connect to a communication network 120 through a wireless connection to a local access or cellular network. The communication network 120 may include one or more public or private networks, e.g., a local access network, wireless carrier, wide-access network, the Internet, etc. The communication module 114 may thereby enable the computer 110 of machine vision system 86 to transmit data to and receive data from the database system 88.

The database system 88 may collect and organize data relating to the physical layer and/or network layer of one or more networks subject to the system 80. Information associated with each network asset 82 in the database system 88 may include the type or identification of the network asset 82, the location of the network asset 82, the identification code encoded in the asset identifier 84 associated with the network asset 82, other network assets 82 to which the network asset 82 is connected, or any other suitable data relating to the network asset 82.

Data maintained by the database system 88 may include a database including data and supporting data structures that store and organize the data. In particular, the database of database system 88 may be arranged with any database organization or structure including, but not limited to, a relational database, a hierarchical database, a network database, or combinations thereof. The database system 88 may also include a database management system in the form of a computer software application executing as instructions on a database server. The database management system may be used to access the data stored in records of the database of database system 88 in response to a query, which may be dynamically determined and executed by the database management system.

Referring now to FIG. 9 , the computer 110 may include a processor 122, a memory 124, an input/output (I/O) interface 126, and a Human Machine Interface (HMI) 128, also referred to as a user interface. The computer 110 may also be operatively coupled to one or more external resources (not shown) via the communication network 120. External resources may include, but are not limited to, servers, databases, mass storage devices, peripheral devices, cloud-based network services, or any other resource that may be used by the computer 110 to implement features of the machine vision system 86.

The processor 122 may include one or more devices selected from microprocessors, micro-controllers, digital signal processors, microcomputers, central processing units, field programmable gate arrays, programmable logic devices, state machines, logic circuits, analog circuits, digital circuits, or any other devices that manipulate signals (analog or digital) based on operational instructions stored in memory 124. Memory 124 may include a single memory device or a plurality of memory devices including, but not limited to, read-only memory (ROM), random access memory (RAM), volatile memory, non-volatile memory, static random access memory (SRAM), dynamic random access memory (DRAM), flash memory, cache memory, or data storage devices such as a hard drive, optical drive, tape drive, volatile or non-volatile solid state device, or any other device capable of storing data.

The processor 122 may operate under the control of an operating system 130 that resides in memory 124. The operating system 130 may manage computer resources so that computer program code embodied as one or more computer software applications, such as an application 132 residing in memory 124, may have instructions executed by the processor 122. One or more data structures 134, such as computer program code, may also reside in memory 124, and may be used by, or executed by, the processor 122, operating system 130, or application 132 to store, data, manipulate data, and/or perform one or more of the methods described herein.

The I/O interface 126 may provide a machine interface that operatively couples the processor 122 to other devices and systems, such as the display 112, communication module 114, and camera 116. The application 132 may thereby work cooperatively with the other devices and systems by communicating via the I/O interface 126 to provide the various features, functions, applications, processes, or modules comprising embodiments of the machine vision system 86. The application 132 may also have program code that is executed by one or more external resources, or otherwise rely on functions or signals provided by other system or network components external to the computer 110. Indeed, given the nearly endless hardware and software configurations possible, persons having ordinary skill in the art will understand that embodiments of the disclosure may include applications that are located externally to the computer 110, distributed among multiple computers or other external resources, or provided by computing resources (hardware and software) that are provided as a service over the network 120, such as a cloud computing service.

The HMI 128, or user interface, may be operatively coupled to the processor 122 of computer 110 to allow a user to interact directly with the computer 110. The HMI 128 may include video or alphanumeric displays, a touch screen, a speaker, and any other suitable audio and visual indicators capable of providing data to the user. The HMI 128 may also include input devices and controls such as an alphanumeric keyboard, a pointing device, keypads, pushbuttons, control knobs, microphones, touchpads, etc., capable of accepting commands or input from the user and transmitting the entered input to the processor 122. The HMI 128 may also include sensors for orientation, acceleration, position (e.g. GPS) or the like.

FIG. 10 depicts an exemplary portion of a data network including a plurality of racks 28 (e.g., three racks), with each rack 28 including a plurality of panels 34 (e.g., 12 panels provided by 12 housings/shelves 32), and each panel 34 including a plurality of ports 36 (e.g., six ports). The portion of the network may be, for example, a greenfield installation of network assets 82 (e.g., servers, routers, switches, etc.) that are in the process of being connected by patch cords 38 in a data center 10 according to a work order.

To this end, a plurality of patch cords 38 (e.g., three cables), each including a pair of terminating connectors 96, 100, may connect one or more pairs of ports 36. As depicted in FIG. 10 , the patch cords 38 typically connect ports 36 in different racks 28. However, aspects of the disclosure are not so limited, and it should be understood that patch cords 38 may also connect ports 36 within a single rack 28. The ports 36 connected by the patch cords 38 may be in different racks 28 in the same row 30, in different rows 30, or in different rooms 26. Thus, any port 36 may be physically connected to any other port 36 by a patch cord 38 as needed in order to implement a physical layer of the data network that enables implementation of a network layer configuration defined in the database system 88.

Each rack 28, housing/shelf 32, panel 34, port 36, and patch cord 38 may include one or more asset identifier 84. Each asset identifier 84 may have encoded therein information that uniquely identifies a network asset 82. In cases where a single network asset (e.g., a patch cord) includes multiple asset identifiers 84, each asset identifier 84 may have the same encoded information. Each asset identifier 84 may be physically associated with its respective network asset 82 by proximity or connection to the network asset 82 and may be logically associated with its respective network asset 82 in the database system 88 by the encoded identifier 84. Although FIG. 10A depicts the racks 28, panels 34, ports 36, and patch cords 38 as each having at least one asset identifier 84, it is not necessary that all network assets 82 be associated with an asset identifier 84.

For example, certain embodiments in accordance with the disclosure may rely solely on identification of ports 36, solely on identification of patch cords 38, or solely on identification of ports 36 and patch cords 38 to determine network connectivity, in which case rack 28 and panel 34 identifiers 84 would be unnecessary. In another embodiment according to the disclosure, ports 36 could be identified based on their position in a panel 34, in which case only the panel 34 would need to be associated with a network identifier 84. In yet another embodiment of the disclosure, panels 34 could be identified based on their position in the rack 28, in which case only the rack 28 would need to be identified, e.g., by a network identifier 84.

In addition to including an asset identifier 84, as discussed above, network assets 82 may also be associated with one or more machine readable electronic tags, such as a RFID tag, NFC tag, or the like (not shown). These electronic tags may be interrogated using a non-directional electromagnetic signal emitted by the machine vision system 86, for example. In response to receiving the interrogation signal, the electronic tags (if present) may enable a machine vision system 86 to identify any network assets 82 that are proximate to the machine vision system 86, e.g., in the same room 26, row 30, or rack 28 as, or within the blast-radius of, the machine vision system 86. For example, a rack 28 could be identified by an electronic tag. Once the rack 28 has been identified, a panel 34 could be identified based on its position within the rack 28 (e.g., third panel 34 from the top of the rack 28), and a port 36 by its position within the panel 34 (e.g., fifth port 36 from the left in the panel 34). Thus, embodiments of the disclosure may include a mix of network assets 82 that are associated with an asset identifier 84 and network assets that are not associated with an asset identifier 84.

Patch cords 38 may include an asset identifier 84 proximate to each connector 96, 100 and one or more asset identifiers 84 along the length of the patch cord 38. Asset identifiers 84 proximate to the connectors 96, 100 may facilitate identification of the patch cord 38 connected to a port 36 by the machine vision system 86. Asset identifiers 84 along the length of the patch cord 38 may facilitate tracing the path of the patch cord 38 between racks 28 by the machine vision system 86. Tracing the path of the patch cord 38 also effectively traces a physical pathway in the data center 10, which may be displayed to a user using the display 112.

FIG. 11A depicts a typical operating environment for the machine vision system 86 in accordance with embodiment of the disclosure. In the depicted embodiment, the machine vision system 86 (e.g., a smartphone 140) is being used by a technician to document connectivity of a rack 28 in a row 30 of racks 28, such as might be found in a data center 10. The camera 116 of machine vision system 86 may have a field of view 118 such that the images captured by the camera 116 cover the rack 28 when the machine vision system 86 is held a predetermined distance (e.g., two meters) from the rack 28. The camera 116 of machine vision system 86 may include a zoom feature that allows the user or the database system 88 to adjust the field of view 118 so that it captures an area of interest by increasing the field of view (to include additional network assets 82 and asset identifiers 84) or decreasing the field of view (to capture more detail of a network asset 82 or asset identifier 84 in the field of view).

The machine vision system 86 may be used to periodically capture images of the rack 28 or other network assets 82 before, during, and after execution of a work order to identify assets 82 which are to be reconfigured, to facilitate proper reconfiguration of the assets 82 according to the work order, to validate that the work order has been executed, and to update the database system 88 so that the database records reflect the physical and network layers of the data network. As mentioned above, although shown as a hand-held device, embodiments of the machine vision system 86 may include a head mounted display, an imaging device temporarily or permanently mounted (e.g., on a tripod, wall, or cabinet frame) to view a portion of the data network, or a robot configured to execute work orders automatically. In some example embodiments, the machine vision system 86 may be mounted on an automated device, such as a roving robot or rail mounted scanner.

In an example embodiment, the machine vision system 86 may be configured to read a first asset identifier 84 associated with rack 28, housing/shelf 32, panel 34, or the like and determine a search matrix associated with the location of asset identifiers 84 associated with ports 36 or connectors 96. In the example depicted in FIG. 11B, a housing/shelf 32 is provided including three panels 34 each having 24 ports 36. The housing/shelf includes a first asset identifier 84A, the panels each include an asset identifier 84B and the ports 36 each include an asset identifier. Similarly, asset identifiers 84D may be affixed to each connector 96.

The asset identifier 84A associated with the housing/shelf 32 and/or the asset identifier 84B associated with the panels 34 may be used an “anchor label.” The anchor label may identify the type of equipment and provide equipment information, including but not limited to serial, number, model number, number of panels 34, configuration or spacing of panels 34, number of ports 36, configuration of ports 36, or other suitable information. In some embodiments, the equipment information may be used to query a database to retrieve further equipment information. For example, a serial number may be used to retrieve the model number, number of panels 34, configuration or spacing of panels 34, number of ports 36, configuration of ports 36. Additionally, the query may return rack location housing/shelf location, panel location, or other information relative to a real world placement of the associated equipment. In some examples the equipment information may include a search matrix, or the configuration information associated with housing 32, panels 34, or ports 36 may be used by the machine vision system to generate a search matrix. The anchor label may be positioned at a known location on the network asset and the search matrix may include locations for each network asset, such as a port 36, relative to the anchor label. In some example embodiments the machine vision system may utilize two or more anchor labels to limit drift of the search matrix as the distance of the port locations increases from the anchor label. The machine vision system 86 may display at least a portion of the search matrix on an I/O interface for the user to move the camera about the network asset to identify asset identifiers 84. For example, the search matrix may be displayed as an augmented reality or virtual reality overlaid on an image or video of the network assets 82. The machine vision system 86 may search one or more search matrix locations to identify asset identifiers 84. The search matrix is discussed in further detail below in reference to FIGS. 11C, 11D, and 16 .

FIG. 12 depicts a flowchart illustrating a process 150 that may be executed in connection with carrying out a work order to install or update one or more network connections. The work order may be for a new greenfield installation in a data center 10, replacement of a piece of IT equipment 18, addition/deletion of a connection in a distribution point 56 of a carrier network 42, or any other action involving connecting data ports 36, 60 with a patch cord 38 or cables 68, 70. In block 152, the process 150 may query a database, such as database system 88, for information identifying logical, physical, or both logical and physical connections in the data network being serviced. This information may include existing logical and physical connections as well as new connections which are to be made according to the work order.

In cases where the work order is related to a greenfield installation, the information may be comprised mostly or entirely of network layer connections. As network assets are installed, and their physical locations and identities become known to the database system 88 based on images including the asset identifiers 84 (e.g., router V is on housing or panel W of rack X, patch cord Y runs from rack X to rack Z, etc.), the database system 88 may provide information on physical layer connections that need to be made to implement the network. In response to receiving a response to the query, the process 150 may display information to the user indicating one or more physical locations of the data network where work is to be done, e.g., information identifying one or more geographic locations (e.g., campus, central office, or distribution point), buildings, rooms, rows, racks, housings/frames/shelves, panels, ports, patch cords, and connectors. In environments in which physical location data is available, such as GPS or indoor positioning systems, these coordinates may also be used to identify the location of network assets.

In block 154, the process 150 may receive an image of a network asset 82, e.g., a rack 28 in a data center 10 or patch-panel in a distribution point 56. The image may include a plurality of network assets 82, e.g., racks 28, ports 36, connectors 96, 100, and patch cords 38, and asset identifiers 84 identifying those assets 82. The image may be captured by the camera 116 of the machine vision system 86 and may include data indicating a position and orientation of the machine vision system 86 when the image was captured. The image may also be one of a plurality of images, e.g., a video stream.

One or more network assets 82 (e.g., connectors, ports, panels, equipment, etc) and asset identifiers 84 may be within the field of view of the camera 116 in multiple images each having a different view or perspective. Multiple views may be obtained, for example, by moving the machine vision system 86 around a connector to obtain an unobstructed view, to gain different perspectives, to remove an obscuration from the field of view, or to mitigate shadows or other sources of image distortion. In a machine vision environment, more than one camera 116 (or camera views using mirrors) may be used to provide multiple perspectives simultaneously.

When multiple images of the network asset 82 are received, the process 150 may generate a three-dimensional (3-D) model of the network asset 82 based on the images, and the orientation and position of the machine vision system 86 when each image was captured. This 3-D model may facilitate identifying asset identifiers 84 and network assets 82, relative positions of identifiers 84 and network assets 82, as well as physical associations between identifiers 84 and network assets 82, and between two or more network assets 82. Physical associations between network assets 82 may include, for example, an association between a connector 96, 100 and a port 36 (e.g., the connector has a relative position or orientation indicating it is connected to the port), an association between a connector 96, 100 and a rack 28 (e.g., the connector is located in the same image as the rack), etc.

In response to receiving the image, the process 150 may proceed to block 156 and identify any asset identifiers 84 and network assets 82 that are recognized in the image. The identification of asset identifiers 84 and network assets 82 may be made using a computer vision-based object recognition algorithm, for example. In an embodiment of the disclosure, the process 150 may also transmit an interrogation signal configured to trigger a response from one or more RFID tags associated with one or more network assets 82. In cases where a network asset 82 is associated with an RFID tag (e.g., the RFID tag is embedded in or otherwise attached to the asset), the response may enable the process 150 to positively identify what network assets 82 are present. In the case of relatively large or widely spaced assets 82 (e.g., a data center rack 28 or room 26), the process 150 may be able to positively identify a network asset 82 in the image or a location of the machine vision system 86 based on data received from an RFID tag alone. Based on this information, the process 150 may be able to determine if an asset identifier 84 has been identified for each asset 82 proximate to the current location. If asset identifiers 84 have not been identified in the image for each network asset 82 present, it may indicate that missing asset identifiers 84 are obscured or outside the field of view 118 of the camera 116 of the machine vision system 86. In either case, the process 150 may alert the user to take corrective action, e.g., by capturing additional images from different vantage points.

In response to identifying the asset identifiers 84 and network assets 82 in the image, the process 150 may proceed to block 158 and associate each asset identifier 84 with a corresponding network asset 82. An asset identifier 84 may be associated with a connector 96, 100, port 36, patch cord 38, rack 28, or other network asset 82, based on a proximity of the asset identifier 84 to the network asset 82, an orientation of the asset identifier 84 relative to the asset 82 (e.g., the asset identifier is aligned with a patch cord 38), or any other suitable method. Certain network assets 82 may also be identified based on their position relative to another network asset 82. For example, ports 36 may be identified based on one or more of a position of the port 36 on a shelf 32 or panel 34 and a position of the shelf 32 or panel 34 in the rack 28.

Associating asset identifiers 84 with network assets 82 may enable the process 150 to identify assets 82 that can be used to complete the work order. For example, the process 150 may identify equipment ports 36 which are to be connected and available pre-connectorized patch cords 38 which provide a path that can be used to create a physical layer connection between two ports 36 identified in the work order. In cases such as a greenfield installation, multiple network assets 82 may be initially available to realize a physical connection that implements a desired network connection, e.g., there may be multiple patch cords 38 that run between the racks 28 in which two ports 36 which are to be connected reside. In other cases where new patch cords 38 are being installed between racks 28, the database may lack information on which network assets 82 are available to make, or that are being used to make, a physical connection until images showing the newly installed assets or physical connection are captured by the machine vision system 86.

In block 160, the process 150 may identify existing connections between network assets 82, e.g., between a connector 96, 100 terminating a patch cord 38 and a port 36 of the network asset 82. The process 150 may identify connections based on proximity and orientation of the asset identifiers 84 and network assets 82 in a similar manner as described above with regard to associating identifiers 84 with the network assets 82.

Once the asset identifiers 84, assets 82, and connections have been identified, the process 150 may proceed to block 162. In block 162, the process 150 may compare the asset identifiers 84, network assets 82, and connections with the physical layer and network layer connections for the network asset 82 indicated by the database system 88. The process 150 may also update the database records to reflect the current physical layer connections and locations of assets 82 in the data network, e.g., which connectors 96, 100 are available in which racks. If any discrepancies are detected (“YES” branch of decision block 164), the process 150 may proceed to block 166 and display indicators identifying network assets 82 (e.g., ports 36, connectors 96, 100, and patch cords 38) which are in a state inconsistent with the database records (e.g., not connected or connected to the wrong port/connector).

For a new installation, initially, there may be a large number of inconsistencies, e.g., a large number of unconnected ports 36. In contrast, for a work order involving moving a subscriber from one service to another, there may only be a few inconsistencies, e.g., one connector plugged into an incorrect port (of the old service provider) and an empty port (of the new service provider) to which the connector is to be moved. In cases where there are a large number of inconsistencies, the device may be used to capture additional images as work progresses to confirm recently made connections agree with the database records, and to indicate which connections still need to be made and which network assets 82 are available to make those connections.

In an embodiment of the disclosure, the indicators may be displayed in real time on the machine vision system 86 in a manner that positively identifies the network assets 82. These indicators may include bounded boxes that display information identifying the network asset 82 and how the asset 82 should be configured. In cases where the state of the network asset 82 is inconsistent with the database records, the indicator may have an additional characteristic (e.g., a different color or intensity) that alerts the user there may be a problem. For example, an indicator associated with a connector 96, 100 connected to a port 36 that does not match the records in the database system 88 and the port 36 to which the connector 96, 100 should be connected, may be displayed with the same distinctive color. The distinctive color may be different from all other indicators so that the user is able to immediately identify which connector 96, 100 is connected incorrectly, and the port 36 to which the connector 96, 100 should be connected.

By way of example, in response to a user selecting a network asset 82 (e.g., a port 36 or connector 96, 100), the process may uniquely identify another asset 82 (e.g., connector 96, 100 or port 36) to which the selected asset 82 should be connected. In cases where a new asset 82 is being commissioned such that only one end of a patch cord 38 is currently connected to a port 36, the indicator may indicate an incomplete connection if the other end of the selected patch cord 38 terminates in a correct location (e.g., correct rack 28 or distribution point 56), and an incorrect connection if the other end of the patch cord 38 terminates in an incorrect location. These states may be indicated by a color, such as green indicating a correct connection, yellow indicating an incomplete connection, and red indicating an incorrect connection.

The process 150 may iteratively capture images, identify asset identifiers 84 and network assets 82, and display indicators until no discrepancies (e.g., no incorrect connections) are found (“NO” branch of decision block 164). In response to no discrepancies being detected between the database records in the database system 88 and the connections at the location being serviced, the process 150 may proceed to block 168, validate the work order, and update the database records in the database system 88 to indicate the work order has been completed. In cases where certain connections are incomplete (e.g., the far end of one or more connections has yet to be made), validation of the connections may not occur until the far end connection has been completed.

Advantageously, updating the database system 88 based on an image of the completed work order at one location (location A) may enable the system to identify which assets should be connected at another location (location B) in the network. For example, the database system 88 may allow the machine vision system 86 to identify the location of the connector 96, 100 at the far end of a patch cord 38 which has been connected a port 36 in the rack 28 at one location. The user may then be instructed to proceed to location B (e.g., another rack 28 in the same room, a different room, or a different building), and capture an image of the network asset 82 to identify the connector 96, 100 and port 36 to be connected. The user may also notify another user at location B that the end of the patch cord 38 at location A has been connected so that the technician at location B can proceed to make the proper connections on their end. These connections may be made based on updated database records downloaded from the database system 88. The system 80 may thereby assist in executing the work order as well as validate that the work order has been completed properly.

FIG. 13 depicts a flowchart illustrating a process 170 that may be executed in connection with decoding asset identifiers 84 in an image. In block 172, the process 170 may identify one or more asset identifiers 84 in an image, e.g., using object recognition. For example, the process 170 may have one or more object models corresponding to potential images of asset identifiers 84 that could appear in an image. Portions of the image may be compared to the object models, and candidate objects selected based on this comparison. The candidate objects may then be further analyzed to determine if an identifier 84 is present, and if so, the object identified as an asset identifier 84.

Once the asset identifiers 84 have been identified, the process 170 may proceed to block 174 and extract an identifier 84 from the image for decoding. Extraction may include identifying an area of the image in which the asset identifier 84 is present, referred to hereafter as the asset identifier area, and processing just those pixels comprising the asset identifier area to decode the asset identifier 84.

In block 176, the process 170 may apply a color correction to the extracted label. To facilitate color correction, each asset identifier 84 may include a color key placed at a predetermined location on the asset identifier 84, e.g., the beginning or end of sequence of colored rings. The color key may have a neutral color (e.g., white or gray) that is not used in any other part of the asset identifier 84. The color key may thereby provide both a reference color for color correction, and a directional key to indicate which direction the asset identifier 84 should be sequenced for decoding. Color correction may also be performed on the full image using a known color reference target. The target may only need to appear in the frame periodically. For example, a gray or red-green-blue (RGB) color reference could be provided on the equipment frame.

For imaging devices that use a RGB color filter array, the color correction may amplify or attenuate each primary color channel in the asset identifier area until each primary color in the header has an appropriate (e.g., equal) amplitude. For example, for a 24-bit color density, a hexadecimal value of FFFF in a color channel may indicate full saturation of the color, and a hexadecimal value of 0000 may indicate no color present in the channel. In this example, the level of each primary color may be adjusted to have a hexadecimal value near the middle of this range, e.g., 8000. Color correction may thereby account for differences in lighting that could otherwise increase the likelihood of a decoding error.

Once the asset identifier area has been color corrected, the process 170 may proceed to block 178 and convert the asset identifier area from a native color space (e.g., RGB) to a color space which is more suitable for processing (e.g., hue, saturation, lightness (HSL), hue, saturation, value (HSV), YCbCr, etc.). The color space to which the asset identifier area converted may be selected to maximize the color difference among the set of colors used for the color bands 102 in the machine-readable digital label 90, for example.

In block 180, the process 170 may apply noise suppression and smoothing to the asset identifier area. This may include convolving the asset identifier area with a mask that produces a low-pass smoothing operation, anisotropic diffusion, outlier pixel identification and removal, or any other suitable noise suppression or smoothing algorithms. Background noise suppression using a smoothing and thresholding technique may improve the robustness of decoding the asset identifier 84.

The process 170 may then proceed to block 182 and detect and recognize the color bands 102 in the machine-readable digital label 90. Color band detection/recognition may include comparing the color value of each pixel in the asset identifier area to known color values which are used in the color-coding scheme. These values may correspond to a primary color (red, green, blue), or a combination of two primary colors (e.g., yellow, cyan, magenta). Pixels within a threshold range of one of these colors may be classified as potentially belonging to a particular color band 102. From here, the color masks for colors of interest may be constructed. Moreover, positions and color composition of asset identifiers 84 in the image may then be recognized.

In block 184, the process 170 may segment the asset identifier 84 into individual color bands 102, and the color in each band 102 may be identified. Segmentation may include determining an orientation of the asset identifier 84 in the asset identifier area and dividing the asset identifier 84 into equal segments along its length. The color of each segment may then be compared to each of the coding colors (e.g., red, green, blue, yellow, cyan, magenta), and the coding color closest to the segment color selected. A color value for each color band in the asset identifier 84 is extracted for the identification of color bands 102. The order of color may then be passed, for example, to a dictionary of lookup tables.

Once the coding color of each segment has been determined, the process 170 may proceed to block 186, and output the digital code corresponding to the sequence of color codes read from identifier area. In the above example in which there are six color codes, the digital code may be a sequence of numbers between 0 and 5, with the number of digits in the sequence equal to the number of color bands in the label. A final output for the final decoded asset identifier value is displayed in any form of user interface. For example, the asset identifier value may be displayed on the handset while the connector is in the field of view, overlaid on the image in real time, or overlaid on a context image in an augmented reality environment.

In block 188, the process 170 may determine if all the asset identifiers 84 detected in the image have been decoded. If all the asset identifiers 84 have not been decoded (“NO” branch of decision block 188), the process may proceed to block 190, select the next asset identifier 84 to decode, and return to block 176. If all the asset identifiers 84 have been decoded (“YES” branch of decision block 188, the process 170 may terminate.

It may be desirable for asset identifier detection and decoding to be effective over many viewpoints and in challenging ambient conditions (e.g., variable lighting, color temperatures and crowded, congested spaces). Advantageously, an application that implements the processes depicted in FIGS. 12 and 13 may reside in any device including a camera 116 and a processor (e.g. a smart phone, tablet, or other machine vision system) configured to capture images for processing in real-time. The use of a camera 116 to capture images using ambient light avoids the need for specialized readers that use laser light or other source of radiation such as a conventional barcode scanner, RFID reader, NFC reader, or other specialized circuitry in the reader or embedded in the network asset.

It should be recognized, however, that in an alternative embodiment, the machine vision system 86 may include a source of electromagnetic energy, such as a flash, LED, or other light source that supplements ambient light or radio frequency (RF) signals that selectively stimulate a response by the asset identifier 84. Other optional features may include an optical filter or supplemental reader to detect signals or increase the accuracy resolution of the decoding process.

In order to track connectivity from the asset identifiers 84, the computer 110 will generally have to determine spatial information that includes the endpoints of the patch cords 38 or other components and the path of the component between the two endpoints. This information can be used to integrate the information available between multiple views (as required to overcome obstruction) to increase the accuracy and completeness of identifying the ends of the patch cord 38, to determine the proximity of these ends to other components, and to determine which ends belong to the same patch cord 38 as well as the spatial route of that patch cord 38.

FIG. 14 depicts an exemplary view 191 including network assets 82 as it may appear when viewed through a machine vision system 86 including an augmented reality feature. To support the augmented reality feature, the machine vision system 86 may include a head mounted display with a front facing camera 116. The head mounted display may include a see-through screen integrated into glasses or goggles that may be worn by the user. The machine vision system 86 may project virtual images onto the see-through screen or, for systems lacking the head mounted display, may add the images to a real-time video display. The augmented reality feature may be non-marker based (e.g., user activated) or marker based. For marker-based systems, the augmented reality feature may be activated in response to the machine vision system 86 detecting an asset identifier 84 within the camera's field of view 118.

The augmented reality feature may include one or more of head tracking, eye tracking, and hand tracking features. Head and eye tracking features may enable the machine vision system 86 to determine where the user is looking, and display information accordingly. For example, the machine vision system 86 may display a bounded box around the asset identifier 84 or network asset 82, and a text box linked to the bounded box including information associated with the asset identifier 84 or asset 82 at which the user is looking. Hand tracking may allow the user to engage the machine vision system 86 by making hand gestures in the camera's field of view 118. By way of example, tapping on an area of the image occupied by a network asset 82 or asset identifier 84 may trigger the machine vision system 86 to display information relating to the network asset 82.

In the exemplary view of FIG. 14 , the network assets 82 in the field of view 118 include a portion of a rack 28, a plurality of panels 34 in the rack 28, a plurality of ports 36, and a plurality of patch cords 38. Based on asset identifiers 84 proximate to a patch cord connector 96, the machine vision system 86 has added one virtual object 192 to a display, e.g., a bounded box around the connector 96. Based on another asset identifier 84, the machine vision system 86 has added another virtual object 194 to the display, e.g., a bounded box around a port 36 into which the connector 96 is to be connected. The machine vision system 86 may thereby identify network assets 96, 36 that are to be connected.

The machine vision system 86 may also add additional virtual objects 196, 198 to the display, e.g., respective information boxes. These virtual objects 196, 198 may be linked to the virtual objects 192, 194 identifying the network assets 96, 36 to provide information related to the respective network assets 96, 36, e.g., information indicating that the connector 96 should be connected to the port 36. The virtual objects 192, 194, 196, 198 may be persistent and pop into and out of view as the camera's field of view 118 moves such that the network asset 82 associated with the virtual objects 192, 194 moves in and out of the field of view 118.

Referring now to FIG. 15 , the machine vision system 86 may indicate the location of the patch cord 38 associated with the work order outside of the rack 28, e.g., using one or more bounded boxes 200 (depicted), by highlighting the patch cord 38, or any other suitable means. This indication may positively identify the path the patch cord 38 takes to the rack 28 at the terminal end of the patch cord 38 being used to execute the work order. The bounded boxes 200 may be located based on detection of one or more asset identifiers 84 located along the length of the patch cord 38. The path may also be determined based on known location of the terminal connector 96, 100 of the patch cord 38, e.g., based on data extracted from an image of the rack 28 at which the patch cord 38 terminates and stored in the database system 88 at the time the patch cord 38 was installed. Once at the terminating rack 28, the machine vision system 86 may identify the connector 100 and port 36 in a manner similar to that discussed above with respect to FIG. 14 . The persistence feature may thereby facilitate the user identifying the correct connector 100 and port 36 at a proximal end of a patch cord 38, follow the patch cord to the rack at which the patch cord terminates, and then identify the terminal connector 100 and port 36.

Automated Association and/or Verification of Network Assets

Turning to FIG. 16 and referring to FIGS. 11B and 11C, an anchor label and search matrix process is provided in process 220. At block 222, the process 220 may receive an image of a network asset 82, e.g., a rack 28 in a data center 10 or patch-panel in a distribution point 56. The image may include a plurality of network assets 82, e.g., racks 28, ports 36, connectors 96, 100, and patch cords 38, and asset identifiers 84 identifying those assets 82. The image may be captured by the camera 116 of the machine vision system 86 and may include data indicating a position and orientation of the machine vision system 86 when the image was captured. The image may also be one of a plurality of images, e.g., a video stream.

One or more network assets 82 (e.g., connectors and ports) and asset identifiers 84 may be within the field of view of the camera 116 in multiple images each having a different view or perspective. Multiple views may be obtained, for example, by moving the machine vision system 86 around a connector to obtain an unobstructed view, to gain different perspectives, to remove an obscuration from the field of view, or to mitigate shadows or other sources of image distortion. In a machine vision environment, more than one camera 116 (or camera views using mirrors) may be used to provide multiple perspectives simultaneously.

When multiple images of the network asset 82 are received, the process 150 may generate a three-dimensional (3-D) model of the network asset 82 based on the images, and the orientation and position of the machine vision system 86 when each image was captured. This 3-D model may facilitate identifying asset identifiers 84 and network assets 82, relative positions of identifiers 84 and network assets 82, as well as physical associations between identifiers 84 and network assets 82, and between two or more network assets 82. Physical associations between network assets 82 may include, for example, an association between a connector 96, 100 and a port 36 (e.g., the connector has a relative position or orientation indicating it is connected to the port), an association between a connector 96, 100 and a rack 28 (e.g., the connector is located in the same image as the rack), etc.

In response to receiving the image, the process 220 may proceed to block 224 and determine if an anchor label is present in the image. As discussed above in reference to FIG. 11B, an anchor label may be an asset identifiers 84 associated with a rack 28, housing/shelf 32, or panel 34. The identification of anchor labels may be made using a computer vision-based object recognition algorithm, for example. In an embodiment of the disclosure, the anchor labels may include a predetermined value identifying them as an anchor label to the machine vision system 86 and/or may include equipment information identifying a rack 28, housing/shelf 32, or panel 34, or the like. In some instances an asset identifier 84 may be utilized as both an anchor label for subcomponents and as a generic identification label when the equipment is a subcomponent of a larger structure. For example, in FIG. 11C the housing 32 includes two housing anchor labels 84A disposed at each upper corner and a panel anchor label 84B disposed at the right upper corner of each of three panels 34. Here the anchor labels 84B associated with the panels 34 are utilized as anchor labels with regard to the asset identifiers 84D associated with the connectors 96 and as asset identifiers to the anchor label 84A associated with the housing 32.

Moving on to block 226, the process 220 may extract equipment information from the anchor label 84A, 84B. The equipment information may include serial, number, model number, number of panels 34, configuration or spacing of panels 34, number of ports 36, configuration of ports 36, or other suitable information. In some embodiments, the equipment information may be used to query an equipment database, at block 228, to retrieve further equipment information. For example, a serial number may be used to receive, at block 230, the model number, number of panels 34, configuration or spacing of panels 34, number of ports 36, configuration of ports 36. Additionally, the query may return rack location housing/shelf location, panel location, or other information relative to a real world placement of the associated equipment.

Additionally or alternatively, after extracting equipment information anchor label 84A, 84B a user may enter additional equipment information utilizing the I/O interface 126 at block 227. The user may identify the model number, number of panels 34, configuration or spacing of panels 34, number of ports 36, configuration of ports 36 rack location housing/shelf location, panel location, or the like. The additional equipment information may then be stored in a memory, such as the equipment database, for later recall, used, and verification, at block 229.

At block 232, the process may generate at least one search matrix based on the equipment information and the location of at least one anchor label 84A, 84B. As depicted in FIG. 11C, each search matrix 302 may include one or more search matrix locations, e.g. bounding areas 306 or boxes offset from the anchor label 84A, 84B. Each of the bounding boxes 306 may define an area in which a network asset 82 is likely to be located, such as a port 36 and/or connector 96, panel 34, housing/shelf 32, or the like. More particularly, the bounding boxes 306 define target locations for the machine vision system 86 to search for an asset identifier. In some example embodiment, the search matrix 302 may include the full area of a rack 28, housing/shelf 32, panel 34, or the like. In other example embodiments, the search area may include a row or column associate with one or more racks 28, housings/shelves 32, panels 34, or the like. In the depicted embodiment, the search matrix 302 includes bounding boxes extending across three panels 34 in a column and including four connector 96 or port 36 locations on each panel 34. The search matrix 302 and bounding boxes 306 may be displayed on the I/O interface 126, such as an augmented reality overlay. The user may move the camera 116 about the network assets 82 to verify the network assets 82, as described below. In some embodiments, the search matrixes 302 may utilize multiple anchor labels 84A, 84B to limit drift of the bounding boxes 306 as the distance from the anchor label 84A, 84B increases. For example, the search matrixes 302 may utilize both anchor labels 84A associated with the housing/shelf 32 to prevent drift of bounding boxes farther from one corner or the other of the housing/shelf 32. Additionally or alternatively, the anchor labels 84B associated with the panels 34 may also be utilized as the primary anchor label or to limit drift. Similar to the bounding boxes utilized for connectors 96 or ports 36, bounding boxes 305 may also be utilized for panel 34 locations, or other equipment.

Once the search matrix 302 is generated, the search matrix 302 may be persistent, e.g. utilized and/or displayed regardless of whether the anchor label 84A, 84B is within the image. The machine vision system 86 may utilize one or more sensors, such as microelectromechanical system (MEMS) sensors, to determine relative movement of the camera in reference to the one or more racks 28, housings/shelves 32, panels 34, anchor labels 84A, 84B, or the like. The relative movement of the camera 116 may, in turn, be used to determine the placement of the search matrix. Additionally or alternatively, the machine vision system 86 may compute position relationship between multiple images that one or more points, or objects, in common. For example, the machine vision system 86 may utilize Visual-Inertial Odometry (VIO) algorithms, Simultaneous Location and Mapping (SLAM) algorithms, or other suitable methods.

At block 234, the process 220 may determine if one or more asset identifiers are present in one or more of the search matrix locations, e.g. bounding boxes 306, 305. The machine vision system 86 may search the image within the bounding boxes 306, 305 to locate an asset identifier 84. In some example embodiments, the machine vision system may identify an asset identifier 84 using only a portion of the label 90, such as redundant coded labels. In some case the machine vision system 86 may also search the area around the bounding box 306, 305, such as plus or minus 2 mm, or other suitable drift area field. In an example embodiment, the machine vision system 86 may be configured to identify a portion of an asset identifier 84 within the bounding box and search the surrounding area to locate the remaining portion of the asset identifier 84.

At block 236, the process 220 may extract asset equipment information from the one or more asset identifiers 84. The equipment information may include, without limitation, a serial number, a model number, or other suitable information.

At block 238, the process 220 may associate the asset equipment information with the one or more search matrix locations. At block 240, the process 220 may store the asset equipment identification and associated search matrix location in a memory, such as the equipment database. Additionally, in some embodiments, the process may also associate the asset equipment information with other network assets 82, such as proximate network assets, which may be used to validate the asset location in the network or search matrix 202. Additionally, the asset equipment information may be associated with larger structural units of the network. For example, the asset equipment information associated with a connector may be associated with a panel 34, housing/shelf 32, rack 28, network location, and/or other suitable network assets. In some example embodiments, a user may enter additional equipment information utilizing the I/O interface 126, which may also be associated with the asset equipment information.

If blocks 238 and 240 had been previously performed, the process 220 may proceed from block 236 to block 242. At block 242, the process may verify the asset equipment information matches logged equipment information associated with the search location. The asset equipment information may be compared extracted at block 236 may be compared to at least a portion, such as a serial number, of the asset equipment information stored in association with the search matrix location. At block 224, the process may cause an indication of the verification status, such as highlighting the search matrix location in green if the verification is a match or red is the verification is not a match. In an example embodiments, the search matrix locations may be outlined, but not filled until the verification has been completed. Turning back to FIG. 11C, the asset equipment information for the first two asset identifiers 84D associated with connectors 96 (not shown) have been verified, as indicated by the shading, whereas the remaining asset identifiers associated with connectors 96 have not yet been verified. Additionally, anchor labels 84A have also been read and verified by the process, however, the anchor labels 84B associated with the panels 34 have not yet been verified. The user, may move the camera of the machine vision system 86 in front of the rack 28, housing/shelf 32, or the like until the each of the network assets 82 have been verified. In some examples, 144, 288, or 576 connector port locations or associated connectors 96 may be read in as little as 30 seconds.

In some example embodiments, the process 220 may update a work order based on the verification status, at block 246. The process for updating work orders is described above in reference to process 150.

At block 248, the process 220 may cause a communication line activation based on a workorder updated as complete. In an example in which, the work order is directed toward installation or repair of a subscriber line or distribution line, or the like, or other suitable work order, the completion of the work order may be indicative of the communication line being available for data communication. The process may communicate to the network the status of the work order and/or the status of the communication line to cause the network to commence transmitting and receiving on the communication line.

FIG. 17 illustrates an image captured by the machine vision system 86 during verification of network assets, such as during process 220. A housing 32 is populated by a plurality of connectors 96 (not shown), each having a label carrier 103 and an asset identifier 84D. In this example, the housing 32 also includes a label carrier 308 extending from a front face of the housing 32. The asset identifier or anchor label 84A associated with the housing is disposed on the label carrier 308, such that the asset identifier 84A associated with the housing 32 are substantially coplanar with the asset identifiers 84D of the connectors. Having the asset identifiers disposed coplanar may reduce search matrix location errors and or drift. The anchor label 84A has been read and used to generate a plurality of search matrixes 302 on a first of three rows in the housing 32. In the depicted example, the search matrixes 302 each include twelve bounding boxes 306 arranged in two rows of six. Each of the bounding boxes 306 has been associated and verified as indicated by the highlighting of the bounding box.

Additionally, a process text box 310 is presented in the lower left corner. Here the process text box 310 includes port number to cable serial number associations. However, other process steps may also be indicated.

In some examples the equipment information may include a search matrix, or the configuration information associated with housing 32, panels 34, or ports 36 may be used by the machine vision system to generate a search matrix. The search matrix may include a locations for each port 36 relative to the anchor label. In some example embodiments the machine vision system may utilize two or more anchor labels to limit drift of the search matrix as the distance of the port locations increases from the anchor label.

As described above, the machine vision system 86 may detect and segment the asset identifiers 84 (e.g., the machine-readable digital labels 90) within the field of view 118 and indicate the location of an identified asset 82 to the user using augmented reality. This detection and segmentation may enable asset identification and digital traceability of the object. One way to accomplish object detection is to use error correction and machine learning algorithms. The use of computer vision and machine learning in particular may allow the machine vision system 86 to account for large variations in the ambient environment expected in a congested data center 10, where hundreds of virtually identical connectors and patch cords are often within the field of view.

The use of machine learning may enable the machine vision system 86 to: (1) detect an individual asset 82 (e.g., connector, port, or patch cord) from a large number of similar assets in the field of view 118; (2) segment the asset identifier 84 in appropriate color spaces; and (3) decode the asset identifier 84 associated with that specific asset 82. The decoded identifier 84 can then be used for digital traceability and intelligent patching within a network environment. Object recognition may be improved over time by allowing the user to provide an indication whether the machine vision system 86 has correctly or incorrectly identified a network asset 82. The machine learning feature may then adjust a neural network or other machine learning algorithm based on this feedback from the user.

Guided Installation of Network Assets

In some example embodiments, the machine vision system 86 may be further configured to assist a technician with installation and validation of a network assets 82, including, but not limited to a new rack, patch panel, module, connector, or the like. A work order may be generated designating the type of network asset to be installed and the location of the installation. Using the anchor labels and assets identifiers, discussed above, the machine vision system may indicate where the new network asset 82 is to be installed in a single step or in a hierarch logical order, such as when access covers, drawers or trays may need to be moved to access the location of installation. Additionally, the machine vision system my provide navigation and validation to the location of installation, such as identifying a building, room, row, rack, etc. The machine vision system 86 may be utilized to read anchor labels and or asset identifiers associated with locations in addition to network equipment and assets to validate an installer's path to the location of installation. Once the installer has installed the equipment or connected a connector, the machine vision system 86 may associate and validate the installation, similar to the process described above in reference to FIG. 16 .

In this regard, FIG. 18 illustrates exemplary 1-U size fiber optic equipment 311 from a front perspective view, that may utilize guide installation to locate a connection location. The fiber optic equipment 311 supports high-density fiber optic modules that support a high fiber optic connection density and bandwidth in a 1-U space, as will be described in greater detail below. The fiber optic equipment 311 may be provided at a data distribution center or central office to support cable-to-cable fiber optic connections and to manage a plurality of fiber optic cable connections. The fiber optic equipment 311 has one or more fiber optic equipment trays 320 that each support one or more fiber optic modules 322. However, the fiber optic equipment 311 could also be adapted to support one or more fiber optic patch panels or other fiber optic equipment that supports fiber optic components and connectivity.

The fiber optic equipment 311 includes a fiber optic equipment chassis 312 (“chassis 312”). The chassis 312 is shown as being installed in a fiber optic equipment rack 314. The fiber optic equipment rack 314 contains two vertical rails 316A, 316B that extend vertically and include a series of apertures 318 for facilitating attachment of the chassis 312 inside the fiber optic equipment rack 314. In some example embodiments, the chassis 312 may include a housing surrounding at least a portion of the chassis 312. The chassis 312 is attached and supported by the fiber optic equipment rack 314 in the form of shelves that are stacked on top of each other within the vertical rails 316A, 316B. As illustrated, the chassis 312 is attached to the vertical rails 316A, 316B. The fiber optic equipment rack 314 may support 1-U-sized shelves, with “U” equal to a standard 1.75 inches in height and nineteen (19) inches in width. In certain applications, the width of “U” may be twenty-three (323) inches. Also, the term fiber optic equipment rack 314 should be understood to include structures that are cabinets, as well. In this embodiment, the chassis 312 is 1-U in size; however, the chassis 312 could be provided in a size greater than 1-U as well, such as 2-U, 4-U, or the like. The chassis 312 depicted is an open chassis. However, in other example embodiments, the chassis 312 may be disposed internal to a housing, which may include a front cover.

The fiber optic equipment 311 includes a plurality of fixed or extendable fiber optic equipment trays 320 that each carries one or more fiber optic modules 322. Each fiber optic equipment tray may include one or more module guides rails configured to slidably receive the fiber optic modules 322. In an example embodiment, the fiber optic modules 322 may be installable from either the front of the fiber optic equipment trays 320 the rear of the fiber optic equipment trays, or both. The chassis 312 and fiber optic equipment trays 320 support fiber optic modules 322 that support high-density fiber optic connection density and/or high density WDM channel density in a given space, including in a 1-U space.

FIG. 18 shows exemplary fiber optic components 323 disposed in the fiber optic modules 322 that support fiber optic connections. For example, the fiber optic components 323 may be fiber optic adapters or fiber optic connectors. This fiber optic module 322 configuration may provide a front opening, wherein fiber optic components 323 can be disposed through the front opening and at a fiber optic connection density of at least one fiber optic connection per approximately 2 mm of width of the front opening of the fiber optic modules 322 for duplex fiber connector, such as and SN connector, sometimes referred to as a Senko Next-generation connector, or an MDC connector, sometimes referred to as a “mini” or “miniature” duplex connector. Reference below to MDC connectors and adaptors, is merely for illustrative purposes and one of ordinary skill in the art would immediately appreciate that other duplex fiber connectors and associated adaptors may also be used. In this example, eighteen (318) duplex fiber optic components may be installed in each fiber optic module 322. The fiber optic equipment trays 320 in this embodiment support up to four (4) of the fiber optic modules 322 in approximately the width of a 1-U space, and three (3) fiber optic equipment trays 320 in the height of a 1-U space for a total of twelve (12) fiber optic modules 322 in a 1-U space. Thus, for example, if eighteen (18) duplex fiber optic components were disposed in each of the twelve (12) fiber optic modules 322 installed in fiber optic equipment trays 320 of the chassis 312 as illustrated in FIG. 318 , a total of four hundred thirty two (432) fiber optic connections, or two hundred sixteen (316) duplex channels (i.e., transmit and receive channels), would be supported by the chassis 312 in a 1-U space.

FIGS. 19 and 20 illustrate a flowchart of a process 400 for guided installation of network assets utilizing the machine vision system 86. At block 402, the machine vision system may receive a work order to install, change position, or remove a network asset 82, including, but not limited to a rack 28, housings/shelves 32, chassis 312, panels 34, cables, patch cords 38, or the like. The work order may be received from a remote processing center or database or may be entered by an installer on the I/O interface 126.

In some instances, the work order or a portion of the work order may be displayed or selected on the I/O interface 126. The work order may include text pictures, or the like indicating to the technician the type of equipment associated with the work order and job, installation, removal, move, etc.

The work order may include an equipment location associated with the location of the installation, position change, or removal. The equipment location may include position identifiers associated with a building or address, a room, a row, rack, rack position, panel, tray, module, port, or other suitable identifiers. In some embodiments, the identifiers may be hierarchical. In an example embodiment, the equipment location may include a network location that is associated with a connectivity position and/or a physical location.

The work order may also include one or more work order assets. The work order assets may identify the type of network asset to be installed, removed, or change position. For example, the work order asset may include a model number or part number associated with a network asset. In some example embodiments, the work order asset may be specific to a particular serial number, batch number, or the like.

At block 404, the machine vision system 86 may determine the equipment location associated with the work order. The machine vision system 86 may extract the equipment location from the work order. In some examples the equipment location information may include a complete physical location and hierarchal position. In other examples, the equipment location may include a physical location without further information. The machine vision system 86 may query a network asset database based on the work order and/or the equipment location. The machine vision system 86 may receive further equipment location information from the network asset database including hierarchical position, additional physical location reference points, or the like. Similarly, if the equipment location is a network location, the machine vison system 86 may query a network asset database based on the work order and/or the network location. The machine vision system 86 may receive further equipment location information from the network asset database including hierarchical position, physical location, reference points, or the like.

At block 406, the machine vision system 86 may cause the equipment location to be displayed on the O/I interface 126. The equipment location may be displayed in one or more formats, including text, a two dimensional map, three dimensional map, or the like. Additionally, the machine vision system 86 may provide dynamic guidance to navigate to the equipment location. For example, the machine vision system may include one or more sensors for position determination, such as two dimensional indoor position sensors, global positioning sensors, accelerometers, microelectromechanical sensors (MEMS), or the like. The machine vision system 86 may determine and provide a route from a current location to the equipment location. In some embodiments, the machine vision system 86 may transmit a current position and the equipment location to a navigational database and receive the route from the navigational database. In some example embodiments, the machine vision system may provide a three dimensional rendering of the network and allow the user to navigate through the rendering to visualize the location in three dimensional space. In some embodiments, the three dimensional rendering may include walls, or other boundaries, which the user can “see through” to determine the equipment location.

In another example embodiment, the machine vision system may utilize a stepwise hierarchical guidance. The machine vision system 86 may provide a first equipment location, associated with a building, room, row, or the like. The machine vision system 86 may provide a subsequent equipment location, such as a rack, tray, module, or the like, in response to an indication of the machine vision system 86 satisfying the first equipment location. In an example embodiment, the machine vision system 86 may utilize current position data to verify satisfaction of the first equipment location. In another example embodiment, the machine vision system may verify satisfaction of the first equipment location based on scanning a location label or asset identifier 84, similar to the process discussed above in reference to FIG. 16 .

At block 408, the machine vision system 86 may receive an image including a network asset 82, e.g., a rack 28, connector 96, 100, and patch cord 38, cable, or the like and an asset identifier 84 identifying the network asset 82. The image may be captured by the camera 116 of the machine vision system 86 and may include data indicating a position and orientation of the machine vision system 86 when the image was captured. The image may also be one of a plurality of images, e.g., a video stream.

In response to receiving the image, the process 400 may proceed to block 410. The machine vision system 86 may extract asset equipment information from the asset identifier 84. The asset equipment information may include serial, number, model number, signal path or connector configuration information, or other suitable information. In some embodiments, the asset equipment information may be used to query an equipment database to retrieve further asset equipment information. For example, a serial number may be used to receive the model number, signal path, or configuration information. In an example embodiment, the asset equipment information may also include process or manufacture information, such as a lot or batch number, date or location of manufacture, or the like. In some example embodiments, the asset equipment information may also include testing data, such as signal loss across a cable, connector, or module, or any other suitable testing data for the network asset.

At block 412, the machine vision system may determine if the asset equipment information satisfies the work order asset. The machine vision system 86 may compare the asset equipment information to the work order asset to determine if the network asset 82 is desired equipment. In some example embodiments multiple part numbers or model numbers may be acceptable, in other examples the work order asset may be a specific part number or model number. If the asset equipment information does not satisfy the work order asset, the machine vision may provide an indication of improper equipment at block 420, such as a text, color, icon, audible sound, or tactile feedback, or other suitable indication. The user may select another network asset and capture an image returning the process to block 408.

If the asset equipment information satisfies the work order asset, the process may associate the work order asset with the asset equipment information. In some example embodiments, the machine vision system 86 may receive additional asset equipment information from the I/O interface 126. For example, the installer may enter additional asset equipment information, or supplement the asset equipment information, including but not limited to color, serial numbers, equipment condition, installation date, or any other suitable information. At block 418, the asset equipment information may be stored in a memory, including the associated work order asset.

At block 420, the machine vision 86 may cause an indication of a work order asset status, such as a color, text, icon, audible sound, tactile feedback, or the like. For example, a chime and green indication for a satisfactory network asset, or a red indication and a buzz for an unsatisfactory network asset, as described above with regard to block 412.

At block 422, the machine vision system 86 may receive a second image including an anchor label 84A. The machine vision system 86 may determine if an anchor label 84A is present in the image. As discussed above in reference to FIG. 11B, an anchor label may be an asset identifiers 84 associated with a rack 28, housing/shelf 32, or panel 34, or the like. The identification of anchor labels may be made using a computer vision-based object recognition algorithm, for example. In an embodiment of the disclosure, the anchor labels 84A may include a predetermined value identifying them as an anchor label to the machine vision system 86 and/or may include equipment information identifying a rack 28, housing/shelf 32, or panel 34, tray 320, module, 322, or the like. In some instances an asset identifier 84 may be utilized as both an anchor label for subcomponents and as a generic identification label when the equipment is a subcomponent of a larger structure. For example, in FIG. 21 the rack 314 includes anchor labels 84A disposed on a rail 316. The anchor labels are disposed to the side of an equipment location before installation, or at installation. In some embodiments, the anchor labels 84A may be disposed on a chassis 312 or a housing, such as a housing front cover. In the depicted example, the anchor labels 84A are disposed on the rails at U space locations. Here the upper and lower U space includes a chassis 312. However, the middle U space is empty. In this example, the chassis 312 have been previously installed and associated with the anchor label 84A and associated U space in the rack 314.

Moving on to block 424, the machine vision system 86 may extract equipment information from the anchor label 84A. The equipment information may include serial, number, model number, number of panels 34, configuration or spacing of panels 34, configuration or spacing of trays 320, configuration or spacing of modules 322, number of ports 36, configuration of ports 36, compatibility data, or other suitable information. In some embodiments, the equipment information may be used to query an equipment database, at block 426, to retrieve further equipment information at block 428. For example, a serial number may be used to receive the model number, number of panels 34, configuration or spacing of panels 34, configuration or spacing of trays 320, configuration or spacing of modules 322, number of ports 36, configuration of ports 36. Additionally, the query may return rack location housing/shelf location, panel location, or other information relative to a real world placement of the associated equipment, which the installer may verify.

At block 430, the machine vision system 86 may determine if the equipment information satisfies the work order equipment. The machine vision system 86 may compare the equipment information to the work order equipment to determine network asset associated with the anchor label is the desired equipment and desired location. If the equipment information does not satisfy the work order asset, the machine vision may provide an indication of improper equipment, such as a text, color, icon, audible sound, or tactile feedback, or other suitable indication. In some embodiments, the machine vision system 86 will return to block 422, with a subsequent image or frame of a video, until the correct equipment information is located. For example, the installer may move the camera 116 to another location and capture an image returning the process to block 422. In some example embodiments, the machine vision system 86 may cause an indication of a work order equipment status, such as a color, text, icon, audible sound, tactile feedback, or the like. For example, a chime and green indication for a satisfactory desired equipment.

At block 432, the machine vision system 86 may generate a first search matrix based on the equipment information and the location of at least one anchor label 84A. As depicted in FIG. 21 , the search matrix 302′ may include one or more search matrix locations, e.g. bounding areas 306′ or boxes offset from the anchor label 84A. Each of the bounding boxes 306′ may define an area in which a network asset 82 or asset identifier 84 is likely to be located, such as a port and/or connector 96, panel 34, housing/shelf 32, tray 320, module 322, or the like. More particularly, the bounding boxes 306′ define target locations for the machine vision system 86 to search for an asset identifier. In some example embodiment, the search matrix 302′ may include the full area of a rack 28, housing/shelf 32, panel 34, tray 320. module 322, or the like. In other example embodiments, the search area may include a row or column associate with one or more racks 28, housings/shelves 32, panels 34, or the like. In the depicted embodiment, the search matrix 302′ includes bounding boxes extending across three trays 34 in a column. The search matrix 302′ and bounding boxes 306′ may be displayed on the I/O interface 126, such as an augmented reality overlay. The user may move the camera 116 about the network assets 82 to install and verify the network assets 82, as described below. As discussed above, the search matrix 302′ may be persistently displayed regardless of the anchor label presence in a current image.

At block 434, the machine vision system 86 may cause an indication of a work order asset location to be displayed in associated with the search matrix 302′. For example, a bounding box 306′ may be highlighted, as depicted in FIG. 21 , an arrow may point to a location associated with the desired work order asset location, or any other suitable indication. In the depicted example, the bounding box 306′ associated with the lowest tray 320 is highlighted in blue to indicate that the asset location in the work order is associated with the tray. This may be particularly advantageous in embodiments in which one level of the hierarchy may obscure the next, such as a port location in a tray that is racked in, or a module location in a housing that is closed. The identification of the work order asset location in the search matrix may guide the installer to the location that which is required in an intuitive manner.

The example depicted in FIGS. 21-23 is merely for illustrative purposes, and the process described herein may be utilized for any network asset including a single layer hierarchy or multiple layer hierarchy. In the depicted example the asset identifier 84E correspond to a tray 320 in the chassis, however, the asset identifiers 84E could also indicate a tray 320 and a module location, such as in the lower chassis 312 of FIG. 21 . In this example the search matrix could extend across the trays 312 in a plurality of columns. Where additional asset identifiers are available in the same hierarchy level, or are visible in multiple hierarchy levels, the machine vision system may indicate the work asset location of the asset identifier at the lowest, e.g. closest to the work location. For example, the machine vision system 86 could identify the module 322 of the tray 320 or in addition to the tray 320, to expedite the process. In other words, the machine vision system may verify one or a plurality of asset identifiers in the work order based on the visibility of the asset identifiers 84 in the images captured by the camera 116.

At block 435, the machine vision system 86 may determine if additional search matrixes will be used to identify the work order asset location, or if the displayed search matrix includes the work order asset location. If the final work order asset location is indicated in the current search matrix, the process may continue at block 350, following process path “B”. If the final work order asset location is not indicated in the current search matrix, the process may continue at block 436 following process path “A”. It is noted that the depicted process includes a single additional search matrix. However, the machine vision system 86 may iteratively, generate search matrixes and indicate the next asset work location until the final asset work order location is visible an indicated.

Following process path “A” at block 436, the machine vision system 86 may receive a third picture including a second asset identifier. For example, the installer may open a housing, rack out a tray, or other wise move the camera 116 or equipment such that the second asset identifier 84E is within the second image. The second asset identifier 84E in the depicted embodiment, corresponds to the indicated work order asset tray 320 of FIG. 21 . The second asset identifier 84E may become visible to the camera by racking out the tray 320 or by changing the camera 116 angle or distance to capture the second asset identifier 84E in the image.

At block 438, the machine vision system 86 may extract second asset equipment information from the second asset identifier 84E. The extraction of the second equipment information may be substantially similar to the extraction of the equipment information at block 424. At block 440 and 442, the machine vision system 86 may query a database based on the second asset identifier 84E and receive additional equipment information from the equipment database, respectively. The query of the database and receipt of additional equipment information based on the second asset identifier 84E may be substantially similar to the query of the database and receipt of additional equipment information based on the anchor label at blocks 426 and 428. At block 444, the machine vision system may determine if the second equipment information satisfies the work order equipment, in a manner substantially similar to determining if the equipment information satisfies the work order equipment at block 430.

In response to the second equipment information satisfying the work order equipment, the machine vision system may generate a second search matrix, at block 446. The second search matrix 302″ may be based on the equipment information and the second asset identifier, including configuration and offsets from the second asset identifier. In the example depicted in FIG. 22 , the second search matrix 302″ includes bounding boxes 306″ for each port in the tray 320. The bounding boxes 306″ are based on both the anchor label 84A and the second asset label 84E associated with the tray 320. In other embodiments the search matrix 302″ may be limited to a single module 322 or adjacent modules, thereby limiting the search area and simplifying the guidance of the installation of the network asset 82. Similarly, the search matrix 302″ may include bounding boxes 306″ around one or a plurality of adapter block, such as an MDC or SN adapter block disposed in a module 322, as depicted in FIG. 24 .

At block 448, the machine vision system may cause an indication of a second work order location to be displayed in association with the search matrix 302″. For example, a bounding box 306″ may be highlighted, as depicted in FIG. 22 , an arrow may point to a location associated with the desired work order asset location, or any other suitable indication. In the depicted example, the bounding box 306″ associated with a port is highlighted in blue to indicate that the asset location in the work order is associated with that specific port. The installer may insert the connector 96 into the port 36 based on the displayed search matrix and work order location. In some embodiments, instructions may be associated and displayed with the indication, such as “install the connector into the identified adaptor port location.”

At block 450, the machine vision system may receive a fourth image including the asset identifier. The fourth image may be captured subsequent to the installation of the network asset 82.

At block 452, the machine vision system may determine if the asset identifier is present in work order asset location. The machine vision system 86 may search the image within the identified bounding box 306″ to locate an asset identifier 84F. In some example embodiments, the machine vision system 86 may identify the asset identifier 84 using only a portion of the label, such as redundant coded labels. In some case the machine vision system 86 may also search the area around the bounding box 306″, such as plus or minus 2 mm, or other suitable drift area field. In an example embodiment, the machine vision system 86 may be configured to identify a portion of an asset identifier 84 within the bounding box and search the surrounding area to locate the remaining portion of the asset identifier 84.

At block 454, the machine vision system 86 may associate the asset equipment information with the search matrix location, e.g. the work order location. At block 456, the machine vision system 86 may store the asset equipment identification and associated search matrix location in a memory, such as the equipment database. Additionally, in some embodiments, the process may also associate the asset equipment information with other network assets 82, such as proximate network assets, which may be used to validate the asset location in the network or search matrix 202. Additionally, the asset equipment information may be associated with larger structural units of the network. For example, the asset equipment information associated with a connector may be associated with a module 322, tray 320, panel 34, housing/shelf 32, rack 28, network location, and/or other suitable network assets. In some example embodiments, a user may enter additional equipment information utilizing the I/O interface 126, which may also be associated with the asset equipment information.

At block 458, the machine vision system 86 may verify the asset equipment information matches logged equipment information associated with the search location. The asset equipment information may be compared extracted at block 410 may be compared to at least a portion, such as a serial number, of the asset equipment information stored in association with the search matrix location. At block 460, the machine vision system 86 may cause an indication of the verification status, such as highlighting the search matrix location in green, if the verification is a match, or red, if the verification is not a match.

In some example embodiments, the machine vision system 86 may update the work order based on the verification status, at block 462. The process for updating work orders is described above in reference to process 150.

At block 464, the machine vision system may cause a communication line activation based on a work order updated as complete. In an example in which, the work order is directed toward installation or repair of a subscriber line or distribution line, or the like, or other suitable work order, the completion of the work order may be indicative of the communication line being available for data communication. The process may communicate to the network the status of the work order and/or the status of the communication line to cause the network to commence transmitting and receiving on the communication line. Similarly, the machine vision system 86 may terminate transmissions on a line to be removed in response to one or more steps of the process being completed, such as indicating the equipment location of the asset to be removed.

In some example embodiments, the verification of one or more network assets may occur during a guided installation. For example, the machine vision system 86 may be configured to perform the verification process 150 described in FIG. 16 for assets identifiers that appear in the images captured by the camera 116. In this manner each installation, or other operation using the machine vision system 86, may provide a passive audit of connections, equipment positions, or the like. Additionally, the machine vision system may perform also perform the association steps of process 150 for network assets that had not been previously associated. As such, the machine vision system 86 may also provide passive mapping of network assets unrelated to the current task.

The example discussed above is directed toward installation and verification of a network asset. However, other processes including moving equipment locations, or removal of network assets is also contemplated. Additionally, the process above may be conducted on opposing ends of a cable sequentially, or as in the case of a patch cord simultaneously.

Connectivity Tracing Using Mixed Reality

Tracking of network assets and verification of installation, movement, and removal provide many advantages to management and repair to a communication network. As discussed above, the machine vision system described herein may be utilized to map a network of passive components. In some example embodiments, the equipment information associated with a signal cable may include a cable signal structure, and/or connector correlation data. This additional correlation data may enable the signal cables to be mapped in a network in a manner similar to the components. The mapping of the signal cables may be applicable to not just passive networks, such as fiber optic networks, but also to traditional electronic signal network cables. The inclusion of signal cables in the mapping of a communication network may substantially reduce the time and complexity of planning and making repairs, additions, or decommissioning equipment and or cables.

In addition, to mapping the communication network across both signal cables and equipment, equipment information including the cable structure may be very advantageous when the connections are not a one for one connection, e.g. connector/adapter TA on first piece of equipment corresponds to connector/adapter TA on a second piece of equipment. For example, the connector arrangement may “shift” from a first connector end to a second connector end. The shift may be a transposition vertically, up or down one or more rows, or horizontally, left or right one or more columns. In some example embodiments the shift may a linear shift, e.g. diagonally by one or more rows and/or columns. In another example the shift may be a shuffle, which reconfigures the inputs and outputs, which are not adjacent and/or are not contiguous. One such example is depicted in FIG. 29 . The shuffle 700 includes three groups of six input fibers mapped to six groups of three output fibers. In another example, the connector correlation may be arbitrary. In some examples, the connector correlation may include one or more types of shifts from the first end to the second end of a cable. The machine vision system may be utilized to guide installation of cables having multiple connections on each end or dissimilar connectors, e.g. MPO to LC, regardless of the human readable labeling or organization schemes. In other words, the installer can scan any label on a cable once it is associated with a work order, and the machine vision system would indicate where to connect the scanned connector. Additionally, the installer may be able to forward or back trace a signal cable from end to end digitally, without need to break connections for testing to confirm a trace. Further, the digital tracing may be accomplished regardless of the number of intermediate connections, splits, fanouts, bundling, or the like. In some example embodiments, the machine vision system may determine one or more connectivity characteristics, such as total signal loss, connection cycles of a particular port, or other suitable information.

FIGS. 25A-25C provide depictions of simplified networks for context. FIG. 25A is a simple direct connect network, such as a fiber optic network. The communication may be between a first and second communication unit, such as a switch 502 or server. The switches 502 may be connected by a signal cable, e.g. a direct connection 504 having a connector 506, such as an MPO connector on each end. This simple network may be small in scale and in physical distance between switches 302, allowing for a simple architecture of direct switch to switch connections. However, as the number of signals and/or signal path complexity increases the number of connections, cables, equipment, and the like may increase exponentially.

Turning to FIG. 25B, point to point a connection architecture utilizing a trunk cable disposed between switch locations is depicted. the switches are connected to a housings 512 including a plurality of patching panels or modules 510, which may consolidate the signal cables into larger signal cables that carry increasing numbers fibers.

FIG. 25C depicts a cross-connect connection architecture. The cross-connection adds an additional patching connection, e.g. point to point connection 504, between two trunk cables connections 508. FIGS. 25A-25C illustrate the differences in connection complexity as additional connections, fanouts, and consolidations occur. FIGS. 26A-26D illustrate the exponential growth in signals and associated signal cables or fibers.

FIG. 26A illustrates the connections and signal cables between two switches 502 connected by a single twelve fiber module 510 on each end of the network. The example module may be a 12 fiber LC module as discussed above in reference to FIG. 18 . Six duplex signal cables connect the switch 502 to the module 510 utilizing LC connectors 506 on each end. The modules 510 are connected by a twelve fiber trunk cable 508 with an MTP connector 506 at each end.

FIG. 26B illustrates the increase in connections when a four modules, such as a tray of modules discussed in FIG. 18 , are connected between the switches 502. The number of LC duplex patch cables 504 increases to 24 cables. The trunk cable 508 includes four MTP connectors 506 and forty-eight fibers.

FIG. 26C illustrates the increase in connections for a 4U housing including 12 trays totaling forty-eight modules. The switch 502 to modules 510 include two hundred eighty-eight duplex LC patch cables 504 including five hundred and seventy-six fibers. The trunk cable 508 includes forty-eight MTP connectors 506 on each end and five hundred seventy-six fibers.

FIG. 26D illustrates the increase in connections for one rack having 10 4U housings. There are two thousand eight hundred eighty duplex LC patch cables 504 with two thousand eight hundred eighty duplex LC connectors 506 on each end connecting the switch 502 to the modules 510. The truck cables 508 connecting the modules include four hundred eighty MTP connectors 506 and five thousand sixty fibers. Additional connector types such as SN connectors or MDC connectors may triple the density of the modules and connections to the switches. This density increase may result in eight thousand six hundred and forty connections between the switch 502 and the modules 510. The cable trunk may include seventeen thousand two hundred and eighty fibers and one thousand four hundred and forty MTP connectors at each end. Further, these number represent a single rack. Datacenters typically include rows of racks, which further expand the number of connections and complexity.

In addition to the complex connections and number of connections, cable also are designed with different lead lengths. In other words, the length of the cable to each connector end may differ, such as to accommodate one or more particular deployments. For example, the cable 504 depicted in FIG. 28 includes four MTP connectors at a first end having substantially the same lead length to a cable jacket 514 enclosing the signal cables. The cable also includes a plurality of LC duplex connectors disposed at the ends of a plurality of cable fanouts or cable drops. Each of the fanouts has a different length, which may reduce material needs and reduce excess cable management. The length differences may be from a common fanout point having different cable lengths, or may have fanouts at different lengths along the larger cable, as depicted. In an example embodiment, a cable 504 entering the top of a rack may be configured for the shortest lengths to connect to modules or ports close to the top of the rack, leaving the longer lengths for connections farther away from the entry point. Similarly, a cable 504 entering a rack form the bottom may be configured for the short lengths to connect near the bottom of the rack, leaving longer lengths for connections closer to the top of the rack.

Current installation procedures are methodical and slow due to the complexity and number of connections. The process described below may enable a significant reduction in the installation time and a greater reliability of connections due to guided installation and verification. Further, once the connections are made the entire network including active components, passive components, and connectivity between the components. Further, the connectivity is not limited to just the connections between components, but also includes a connectivity structure of the cables, referred to herein as a “connectivity matrix.” The connectivity matrix may be correlation between each connector of a signal cable assembly. In some embodiments, the connectivity matrix may include connector shifts between a first side and a second side, including to vertical shifts, horizontal shifts, diagonal shifts. Additionally or alternatively, the connectivity matrix may include a shift between the first side and a second side, where a shift includes a complex differentiation of signals at the connectors at each end. Once the cable is connected, the connectivity matrix may include the equipment that is connected to each connector, and further downstream connections in the communication network.

FIG. 27 illustrates flow chart of a process 600 of providing connectivity tracing in a network. The process may start at block 602, in which the machine vision system 86 may receive an image of a cable, such as a patch cable 504 or a trunk cable 508, or other signal cable in a communication network. The image may be captured by the camera 116 of the machine vision system 86 and may include data indicating a position and orientation of the machine vision system 86 when the image was captured. The image may also be one of a plurality of images, e.g., a video stream.

Moving on to block 604, the machine vision system 86 may extract equipment information from the asset identifier 84 associated with the cable. The equipment information may include serial, number, model number, number of connectors 506 at each end, type of connectors 506, configuration or signal path between connectors 506, or other suitable information. In some embodiments, the equipment information may be used to query an equipment database, at block 612, to retrieve further equipment information. For example, a serial number may be used to receive, at block 614, the model number, number of connectors 506, type of connectors 506, configuration or signal path between connectors 506, or the like.

If the cable is a new cable or new connector identifier associated with a cable, the process may continue at “C” to the guided installation process 400, block 412, depicted in FIG. 19 . The guided installation process 400 may enable connection of the cable or a connection of a connector of the cable and verification of desire connection, as discussed above. If the cable has been previously installed and verified, the process 600 may continue to block 616. Alternatively, at the completion of verification of the cable installation or connector connection at block 458, the guided installation process 400 may return to block 616 at “D”.

At block 616, the machine vision system may generate a connectivity matrix based on the equipment information. the connectivity may include the cable, including the associated signal paths, and connectors 506. Additionally, the connectivity matrix may include connections to various equipment, such as modules 510, 322, housings 510, chassis 310, shelf 32, or panel 34, or the like. In an example embodiment, the connectivity matrix may include cable architectures and connections to any or all cables and equipment in the network. The connectivity matrix may be two dimensional connectivity trace mapping or may incorporate three dimensional location information and relative positions between network components as a three dimensional map of cables, connections, and network assets.

At block 618, the machine vision system 86 may cause at least a portion of the connectivity matrix to be displayed on the I/O interface 126. The machine vision system 86 may display, the cable and/or connectors of the cable, the cable and directly connected equipment, a region of a network associated with the cable, the entire network, or any suitable portion of the network. The connectivity matrix include text, color coding, bounding boxes, line diagrams, or any other suitable representation of the cable, connectors, equipment, or the like in the network.

In an example embodiment, the connection matrix may be an overlay displayed over the equipment, such as the cable and associated connections in the image. In another example embodiment, the overlay may indicate a connector on the opposite side of a piece of equipment. For example, the connectivity matrix may display the LC connection number on the front of a module 322 and the corresponding MTP connection on a back of the module 322. In some example embodiments, the machine vision system may overlay the connectivity matrix over a label plate, such as a housing cover label plate, or the like. Additionally or alternatively, the connectivity matrix may be independent of any image received by the camera, such as a virtual network or diagram. In some example embodiments the connectivity matrix may be interactive, such that the user may zoom closer to, or away from, one or more network assets 82. The machine vision system 86 may display more or less connections, equipment, or details, based on the scale of the zoom. Additionally or alternatively, the connectivity matrix may be rendered as a three dimensional map including relative positions in space and within one or more building, rooms, or the like.

At block 620, the machine vision system may receive a user input form the I/O interface 126 indicating a selected at least one cable or connection in the connectivity matrix. The user may select cables, connections, or equipment in the connectivity matrix individually, as a group, or the like. For example, the user may use a cursor or touch screen to select one or more cables, connections, and/or equipment.

At block 620, the machine vision system 86 may cause a location associated with the selected one cable, connection, equipment to be displayed on the I/O interface 126. The location may include a text locations, such as a room number, row number, rack number, housing number, tray number, module number, or the like. The location may also be a coordinate position, such as a two dimensional indoor navigation system coordinate or global positioning system coordinate. Additionally or alternatively, the location may be displayed on a two dimensional or three dimensional map.

At block 622, the machine vision system 86 may cause one or more navigational instructions to be displayed on the I/O interface 126 based on a current location and the location associated with the selected at least on cable or connection. The machine vision system 86 may determine a route between a current location and the location associated with the selected cable, connection, or equipment. The machine vision system 86 may display the route, a portion of the route, turn by turn navigation, or the like on the I/O interface 126. Additionally or alternatively, the machine vision system 86 may determine that the current location is proximate to the location of the selected cable, connection, or equipment. The machine vision system 86 may display a directional indicator, such as an arrow highlighted edge, or the like to direct the user to move the camera 116 toward the selected cable, connection, or equipment. Additionally or alternatively, the location may be displayed on the display as a direction indicator or a trail of breadcrumbs providing the direction the user should travel toward the location. The process may continue to guide the user toward the selected cable, connection, or equipment using the guided installation process 400 at “E”, block 408.

In some example embodiments, the machine vision system 86 may be utilized to determine one or more connectivity characteristics associated with network assets 82. For example, the machine vision system 86 may determine clean connection characteristics, enabling a user to determine if a port, cable, connector, or the like is serviceable. At block 624, the machine vision system 86 may determine a connectivity characteristic associated with the selected at least one cable, connection, or equipment in the connectivity matrix. For example, the machine vision system may determine the number of times a port or connector has been connected and/or disconnected. In an example embodiment, the machine vision system 86 may utilize equipment information associated with the selected at least one cable, connection, or equipment, such as test data to determine a total characteristic or signal path characteristic, such as signal loss from point to point. In a further example, the machine vision system may determine the time that a port has been open, e.g. no dust cap installed. The machine vision system may display the connectivity characteristics on the I/O interface 126 automatically or in response to a user input selecting one or more connectivity characteristics.

The processes 150, 170, 220, 400, and 600 are for illustrative purposes. The steps or blocks of the flow charts may be performed in alterative orders based on the received inputs and input orders. In some example embodiments, one or more steps or blocks may be optional. Additionally, references to a first, second, third, or fourth image are also for illustrative purposes. The images may be separately captured images or may the same image for multiple steps or blocks.

The process 600 as described above may be used for installation of a cable, removal of a cable, or physical verification of one or more network asset. Additional use flow paths through process 150, 400, or 600 may be utilized based on entering the process or exiting the process at different points. A repair use case is provided below to illustrate one such example. A user may receive an indication of a network error, such as a communication disruption between a first server or switch 502 and a second server or switch 502. In a typical datacenter the technician may locate the switch and then manually trace a cable and perform one or more tests to locate and repair the communication path. However, physical tracing may be difficult due to consolidation of signals in to larger fibers, passing through bulkheads, connections through equipment, or the like.

In an example embodiment, the machine vision system may enter process 600 at block 618, via path “F”, based on the connectivity matrix being previously generated during one or more of processes 150, 400, and 600. The machine vision may cause at lease a portion of the connectivity matrix to be displayed on the O/I based on a user input, such as matrix mapping request. The matrix mapping request may be general to the network, or more specific to a portion of the network, network asset, cable, or the like.

At block 620, the user input may identify a point to point connection map from the first switch 502 to the second switch 502. The point to point connection map may include each of the cables, connections, and network assets disposed between the first and second switches 502. The machine vision system 86 may receive further user input from the user interface identifying one or more cables, connections, or network assets, such as a convenient point to half split the network. The machine vision system 86 may guide the technician to the location of cable, connection, or network asset via blocks 620 and 622 proceeding to process path E.

Once the technician identifies the cable, connection, or equipment, the technician may test one or both sides of the point to point connection map. For example, the technician may perform an optical time domain reflectometer (OTDR) test, to determine if there is a signal path anomaly, such as excessive loss, or signal break. The OTDR may also provide the technician with the distance to the anomaly. The technician may reenter the process 600 at block 618 to cause the display of a portion of the connectivity matrix, such as the point to point connection map. The utilizing the equipment information associated with cables connections, and network assists, such as cable lengths, fiber length internal to modules, or the like, the technician may determine at, or in, which of the cables, connections, or network assets the anomaly occurs. The machine vision system may then receive a user input selecting the faulty component and guide the technician to the component via blocks 620 and 622 and process path E. Once the technician locates the faulty component, additional confirmation tests may be performed and a repair executed.

In an example embodiment, a machine vision system is provided including a camera, a processor, and a device memory including computer program code stored thereon. The computer program code is configured, when executed by the processor, to receive an image, from the camera, including at least one readable digital label associated with communication equipment, determine if an anchor label is present in the image, receive equipment information based on the anchor label, and generate a search matrix based on the equipment information and the anchor label, wherein the search matrix comprises one or more search matrix locations of assets associated with the communication equipment.

In some example embodiments, the assets comprise one or more of an equipment rack, a shelf, a panel, a port, a connector, or a cable. In an example embodiment, the computer program code is further configured to cause the processor to identify the equipment information in the anchor label. In some example embodiments, the computer program code is further configured to cause the processor to query an equipment database based on the anchor label and receive the equipment information from the database In an example embodiment, the machine vision system also includes a user interface and the computer program code is further configured to cause the processor to cause the search matrix to be displayed on the user interface overlaid on the image. In some example embodiments, the machine vision system also includes a user interface and the computer program code is further configured to cause the processor to receive additional equipment information from the user interface and cause the additional equipment information to be stored in an equipment database. In an example embodiment, the computer program code is further configured to cause the processor to determine if one or more asset identifiers are present in the search matrix locations. In some example embodiments, the computer program code is further configured to cause the processor to extract asset equipment information from the one or more asset identifiers. In an example embodiment, the computer program code is further configured to cause the processor to associate the asset equipment information with a search matrix location and cause the asset equipment information and associated search matrix location to be stored in a memory. In some example embodiments, the computer program code is further configured to cause the processor to verify the asset equipment information matches logged equipment information associated with the search matrix locations. In an example embodiment, the computer program code is further configured to cause the processor to cause an indication of a verification status to be displayed on the user interface. In some example embodiments, the computer program code is further configured to cause the processor to update a work order based on a verification status. In an example embodiment, the computer program code is further configured to cause the processor to cause a communication line activation based on the work order being updated.

In another example embodiment, a method of tracking assets of communication equipment is provided.

In a further example embodiment, a machine vision system is provided including a camera, a user interface, a processor, and a device memory including computer program code stored thereon. The computer program code is configured, when executed by the processor, to receive an image including an asset identifier, extract asset equipment information from the asset identifier, determine if the asset equipment information satisfies a work order asset, receive a second image including an anchor label, extract equipment information from the anchor label, generate a search matrix based on the equipment information and the anchor label location, and cause an indication of a work order asset location to be displayed on the user interface in association with the search matrix.

In an example embodiment, the computer program code is further configured to cause the processor to associate the work order asset with the asset equipment information and store the asset equipment information in a memory. In some example embodiments, the computer program code is further configured to cause the processor to receive additional asset equipment information from the user interface. In an example embodiment, the computer program code is further configured to cause the processor to cause an indication of the work order asset status. In some example embodiments, the computer program code is further configured to cause the processor to receive a work order, determine an location associated with the work order, and cause the equipment location to be displayed on the user interface. In an example embodiment, the computer program code is further configured to cause the processor to extract equipment information from the anchor label and determine if the equipment information satisfies a work order equipment. In some example embodiments, the computer program code is further configured to cause the processor to query an equipment database based on the anchor label and receive additional equipment information from the equipment database. In an example embodiment, the computer program code is further configured to cause the processor to receive a third image including the asset identifier, determine if the asset identifier is present in the work order asset location, and associate the asset equipment information with the work order asset location. In some example embodiments, the computer program code is further configured to cause the processor to store the asset information and the associated work order asset location to a memory. In an example embodiment, the computer program code is further configured to cause the processor to verify the asset equipment information matches equipment information associated with the work order location and cause an indication of a verification status to be displayed on the user interface. In some example embodiments, the computer program code is further configured to cause the processor to update a work order based on the verification status. In an example embodiment, the computer program code is further configured to cause the processor to cause line activation based on the work order being updated as complete. In some example embodiments, the computer program code is further configured to cause the processor to receive a third image including a second asset identifier, extract second asset equipment information from the second asset identifier, generate a second search matrix based on the equipment information and the second asset identifier location and cause an indication of a second work order location to be displayed in association with the second search matrix. In an example embodiment, the computer program code is further configured to cause the processor to query an equipment database based on the second asset identifier and receive additional second asset equipment information from the equipment database. In some example embodiments, the computer program code is further configured to cause the processor to determine if the second asset equipment information satisfies the work order equipment. In an example embodiment, the computer program code is further configured to cause the processor to receive a fourth image including the asset identifier, determine if the asset identifier is present in the work order asset location, and associate the asset equipment information with the work order asset location.

In yet a further embodiment a method of guiding installation of communication equipment assets is provided.

In still another example embodiment, a machine vision system is provided including a camera, a user interface, a processor, and a device memory including computer program code stored thereon. The computer program code is configured, when executed by the processor, to receive an image including an asset identifier associated with a cable, extract asset equipment information from the asset identifier associated with the cable, generate a connectivity matrix based on the equipment information, and cause at least a portion of the connectivity matrix to be displayed on a user interface.

In an example embodiment, the computer program code is further configured to cause the processor to receive a user input, from the user interface, indicating a selected at least one cable of connection in the connectivity matrix and cause a location associated with the selected at least one cable or connection to be displayed on the user interface. In some example embodiments, the computer program code is further configured to cause one or more navigational instructions to be displayed on the user interface based on a current location and the location associated with the selected at least one cable or connection. In an example embodiment, the computer program code is further configured to cause the processor to query an equipment database based on the asset identifier or equipment information and receive additional equipment information from the equipment database. In some example embodiments, the computer program code is further configured to cause the processor to receive additional equipment information form a user interface and store the additional equipment information in a memory. In an example embodiment, the computer program code is further configured to cause the processor to receive work order information and cause the work order information in a memory. In some example embodiments, the computer program code is further configured to cause the processor to determine if the asset equipment information satisfies a work order asset. In an example embodiment, the computer program code is further configure to cause the processor to receive a second image including an anchor label, extract equipment information from the anchor label, generate a search matrix based on the equipment information and the anchor label location, and cause an indication of a work order asset location to be displayed on the user interface in association with the search matrix. In some example embodiments, the computer program code is further configured to cause the processor to associate the work order asset with the asset equipment information and store the asset equipment information in a memory. In an example embodiment, the computer program code is further configured to cause the processor to cause an indication of the work order asset status. In some example embodiments, the computer program code is further configured to cause the processor to receive a third image including the asset identifier, determine if the asset identifier is present in the work order asset location, and associate the asset equipment information with the work order asset location. In an example embodiment, the computer program code is further configured to cause the processor to store the asset information and the associated work order asset location to a memory.

In yet another embodiment, a method of providing connectivity tracing in a network is provided.

Although the above disclosure describes embodiments of the system 80 in great detail with respect to management of the physical layer of a data network, the scope of the disclosure is not so limited. For example, aspects of the system could also be used to manage laboratory equipment for chemical, biochemical, life science, or medical applications that include tubing which connects sample containers, culture vessels, reactor chambers, etc. Aspects of the system may also be used to track assets in the form of tubing, wiring, and plumbing in buildings, manufacturing plants, refineries, or any other application in which physical connections between multiple assets are used. Accordingly, aspects of the present disclosure should be limited to data network applications, but apply to a broader range of potential applications, such as power systems (with power cables), medical systems (with medical tubing or cables), or the like.

Thus, those skilled in the art will appreciate that other modifications and variations can be made without departing from the spirit or scope of the disclosure. Since modifications, combinations, sub-combinations, and variations of the disclosed embodiments incorporating the spirit and substance of the disclosure may occur to persons skilled in the art, the disclosure should be construed to include everything within the scope of the appended claims and their equivalents. 

What is claimed is:
 1. A machine vision system comprising: a camera; a user interface; a processor; and a system memory including computer program code stored thereon, wherein the computer program code is configured, when executed by the processor, to: receive an image, from the camera, including an asset identifier; extract asset equipment information from the asset identifier; determine if the asset equipment information satisfies a work order asset; receive a second image including an anchor label; extract equipment information from the anchor label; generate a search matrix based on the equipment information and an anchor label location; and cause an indication of a work order asset location to be displayed on the user interface in association with the search matrix.
 2. The machine vision system of claim 1, wherein the computer program code is further configured to cause the processor to: associate the work order asset with the asset equipment information; and store the asset equipment information in a memory.
 3. The machine vision system of claim 2, wherein the computer program code is further configured to cause the processor to: receive additional asset equipment information from the user interface.
 4. The machine vision system of claim 1, wherein the computer program code is further configured to cause the processor to: cause an indication of a work order asset status.
 5. The machine vision system of claim 1, wherein the computer program code is further configured to cause the processor to: receive a work order; determine a location associated with the work order; and cause the location to be displayed on the user interface.
 6. The machine vision system of claim 1, wherein the computer program code is further configured to cause the processor to: determine if the equipment information satisfies a work order equipment.
 7. The machine vision system of claim 6, wherein the computer program code is further configured to cause the processor to: query an equipment database based on the anchor label; and receive additional equipment information from the equipment database.
 8. The machine vision system of claim 1, wherein the computer program code is further configured to cause the processor to: receive a third image including the asset identifier; determine if the asset identifier is present in the work order asset location; and associate the asset equipment information with the work order asset location.
 9. The machine vision system of claim 8, wherein the computer program code is further configured to cause the processor to: store the asset equipment information and the associated work order asset location to a memory.
 10. The machine vision system of claim 8, wherein the computer program code is further configured to cause the processor to: verify the asset equipment information matches equipment information associated with the work order asset location; and cause an indication of a verification status to be displayed on the user interface.
 11. The machine vision system of claim 10, wherein the computer program code is further configured to cause the processor to: update a work order based on the verification status.
 12. The machine vision system of claim 11, wherein the computer program code is further configured to cause the processor to: cause line activation based on the work order being updated as complete.
 13. The machine vision system of claim 1, wherein the computer program code is further configured to cause the processor to: receive a third image including a second asset identifier; extract second asset equipment information from the second asset identifier; generate a second search matrix based on the equipment information and a second asset identifier location; and cause an indication of a second work order location to be displayed in association with the second search matrix.
 14. The machine vision system of claim 13, wherein the computer program code is further configured to cause the processor to: query an equipment database based on the second asset identifier; and receive additional second asset equipment information from the equipment database.
 15. The machine vision system of claim 13, wherein the computer program code is further configured to cause the processor to: determine if the second asset equipment information satisfies the work order equipment.
 16. The machine vision system of claim 15, wherein the computer program code is further configured to cause the processor to: receive a fourth image including the asset identifier; determine if the asset identifier is present in the work order asset location; and associate the asset equipment information with the work order asset location.
 17. A method of guiding installation of communication equipment assets, comprising: receiving an image, from a camera, including an asset identifier; extracting asset equipment information from the asset identifier; determining if the asset equipment information satisfies a work order asset; receiving a second image including an anchor label; extracting equipment information from the anchor label; generating, by a processor, a search matrix based on the equipment information and an anchor label location; and causing an indication of a work order asset location to be displayed on a user interface in association with the search matrix.
 18. The method of claim 17 further comprising: associating the work order asset with the asset equipment information; and store the asset equipment information in a memory.
 19. The method of claim 18 further comprising: receiving additional asset equipment information from the user interface.
 20. The method of claim 17 further comprising: causing an indication of a work order asset status. 